Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 36 appraisals
Journal of medicinal chemistry
Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors
Developing potent, selective small-molecule inhibitors remains a major challenge in drug discovery. ALDH3A1, a detoxifying aldehyde dehydrogenase isoform implicated in cancer and neurodegeneration, is a promising yet underexplored therapeutic target. To accelerate inhibitor optimization, we developed an AI-guided, reaction-based hit-to-lead workflow combining sequential reaction enumeration, pharmacophore-informed docking, and predictive modeling to support scalable SAR expansion. Applied to ALDH3A1, two rounds of enumeration using Enamine building blocks generated about 250,000 virtual analogues. Combined deep learning and docking-based triage prioritized 150 compounds for synthesis, leading to a roughly 1,000-fold improvement in biochemical potency from 1.41 μM to 1 nM for NCATS-SM0707, together with 4 nM cellular activity for NCATS-SM0708. Crucially, this methodology can be expanded by applying various chemical reactions at different positions. This study highlights CSAR as a scalable, generalizable complementary strategy to accelerate hit optimization through reaction-based enumeration and AI-guided prioritization of larger synthetically accessible chemical space.
13 June 2026
Read appraisal →Journal of computer-aided molecular design
Target-aware molecule SMILES generation using a large language model with retrieval-augmented generation, multi-turn memory, and a predictive model
Given the vastness of chemical space and the cost and time requirements of high-throughput screening, resource-efficient computational strategies are needed to prioritize candidate drug molecules. Here, we evaluated whether an open-weight large language model (LLM) augmented with retrieval-augmented generation (RAG), multi-turn memory, and a pretrained drug-target interaction predictor can generate target-aware molecules without task-specific retraining. Protein-ligand-pKi examples retrieved from BindingDB, Davis, and KIBA were used as contextual guidance, while DeepPurpose provided the optimization signal during iterative SMILES refinement. Our approach produced a statistically significant increase in predicted pKi across successive multi-turn memory iterations, indicating that retrieval- and memory-guided refinement may improve target-conditioned molecular generation. The proposed framework also showed favorable molecular generation characteristics, with novelty reaching 100%, diversity up to 0.882, and uniqueness up to 1.0, suggesting that retrieval did not reduce the process to simple reproduction of known ligands but instead supported exploration of new regions of chemical space. In addition, the approach yielded supportive docking results consistent with the generation of chemically relevant candidate ligands. However, the increase in predicted affinity was accompanied by trade-offs in molecular quality, including reduced validity and drug-likeness in later iterations. Taken together, these findings suggest that this framework provides a flexible and comparatively resource-efficient strategy for target-aware de novo molecular design, while further multi-objective optimization and independent validation remain necessary.
31 May 2026
Read appraisal →Journal of chemical information and modeling
Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer
Cervical cancer remains a major global health challenge, where dysregulated JAK2 signaling constitutes a key molecular driver. Nevertheless, selective small-molecule JAK2 inhibitors for HPV-positive cervical cancer are still limited. Here, we integrated biochemical assays, QSAR-machine learning, and molecular dynamics simulations to identify potent JAK2 inhibitors. A series of naphthalene-based derivatives, including hydroxynaphthalenamide and phosphorylated dihydronaphthylamide analogs, were evaluated for cytotoxicity in HeLa cells and JAK2 kinase inhibition. Several compounds exhibited selective cytotoxicity with minimal activity toward normal fibroblasts, among which 2q and 2s showed low-nanomolar JAK2 inhibition and strong apoptosis induction through suppression of the JAK2/STAT3/STAT5 pro-tumorigenic signaling pathway. To accelerate hit identification, a QSAR-machine learning (QSAR-ML) framework was employed to prioritize 13 newly designed derivatives. Among three ensemble boosting models, the Categorical Boosting (CB) model demonstrated the strongest predictive capability, achieving a high R2 of 0.955 for the training set and a low RMSE of 0.156 for the test set. This model successfully identified five active candidates with strong prediction-experiment agreement (MAPE = 2.6-14.4%), with D4 and D13 meeting drug-likeness criteria and displaying potent nanomolar JAK2 inhibition. Finally, 1-μs molecular dynamics simulations revealed that hydrophobic contacts and hydrogen bonding cooperatively stabilize these inhibitors within the JAK2 ATP-binding pocket. Collectively, these findings establish a QSAR-ML-guided strategy for accelerating JAK2 inhibitor discovery and highlight naphthalene-based scaffolds as promising leads for targeted cervical cancer therapy.
27 May 2026
Read appraisal →ChemMedChem
A 24-Day MicroCycle Journey to Interleukin-17 Inhibitors: From Library Design to Central Core Prioritization and Capping Group Identification
We herein report an application of the MicroCycle platform, which leverages advanced machine learning models, automation, and miniaturization of processes to accelerate the exploration of chemical space through libraries. This approach was applied to a novel thiazole-based series targeting interleukin-17, enabling rapid prioritization of central cores and identification of compelling new capping groups, thereby guiding further optimization efforts.
27 May 2026
Read appraisal →Journal of chemical theory and computation
MolSculptor: An Adaptive Diffusion-Evolution Framework Enabling Generative Drug Design for Multitarget Affinity and Selectivity
The rational design of molecules with tailored activity/selectivity across multiple protein targets is crucial for developing therapies for complex diseases like cancer, yet it remains a formidable challenge. While deep generative models show immense promise, their application to these tasks faces fundamental challenges, as they struggle to incorporate the structural information on multiple distinct protein pockets and require vast multitarget data sets or specialized expert knowledge that are rarely available. Here we introduce MolSculptor, an adaptive diffusion-evolution framework designed to generate inhibitors for any combination of on- and off-targets, circumventing the need for target-specific training data or prior expert knowledge. MolSculptor unifies both de novo design and lead optimization, provides a versatile workflow applicable to different stages of drug discovery, and allows for direct conditioning on key drug-like properties. It integrates a 3D-aware surrogate model to enable flexible guidance for any set of specified on- and off-targets. Furthermore, MolSculptor employs an active learning protocol to adaptively refine this guidance, ensuring high performance even in data-scarce scenarios. We demonstrate MolSculptor on a series of challenging multitarget and selective inhibitor design tasks, where it significantly outperforms state-of-the-art methods in generating high-quality candidates that satisfy all complex constraints. Notably, many of the generated molecules exhibit predicted affinity profiles superior to those of experimentally validated references. Using MolSculptor, we successfully designed and synthesized a novel and potent dual-target inhibitor for castration-resistant prostate cancer (CRPC), whose inhibitory activity was confirmed through wet-lab validation. MolSculptor provides a powerful and generalizable paradigm for designing ligands with complex, multitarget activity profiles, paving the way for data-efficient solutions to complex therapeutic problems.
27 May 2026
Read appraisal →Journal of chemical theory and computation
MolXProt: A Cross-Attention Transformer-Based Graph Neural Network for Protein-Ligand Binding Affinity Prediction
Accurate and fast prediction of drug-target binding affinities (DTAs) is key for drug discovery; however, many methods, such as docking and empirical scoring, fail when generalizing to unseen cases. In this study, we introduced the MolXProt architecture, a novel transformer-based graph neural network that integrates graph ligand representations with protein language models using bidirectional multihead cross-attention. Our model is shown to be scalable to over 100000 protein-ligand pairs of mixed data sets, achieving 50% of predictions within ±1.0 kcal/mol and 80% within ±2.0 kcal/mol. We show that the architecture can explicitly learn residue-atom interactions while being computationally friendly via protein-token compression. By mapping token-residue interactions, we demonstrated that the model learns key binding pocket residues in benchmark complexes, such as CDK2-Staurosporine and DHFR-Methotrexate, but under-represents the hydrogen-bonding networks. Our calibration bias analysis revealed that the model overpredicted strong binders and underpredicted weak binders, which are linked to data imbalances and heteroscedastic noise. A simple posthoc isotonic correction partially mitigated the bias. Latent space analysis showed that the model learned continuous binding affinity manifolds without split leakage. Our work highlights a novel architecture that offers unique insights into binding mechanisms via transformer-based cross-attention and is computationally inexpensive.
27 May 2026
Read appraisal →Journal of chemical theory and computation
Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework
Computational prediction of allosteric binding sites in protein structures remains a persistent challenge, as these regulatory pockets evade detection by both sequence-based and structure-based algorithms. Both computational and physical origins of this predictive asymmetry remain insufficiently understood. In this study, we systematically examine the determinants of binding site predictability using a dual framework that integrates a fine-tuned protein language model and the structure-based method P2Rank as complementary tools probing a diverse data set of 453 human kinases, together with a physics-based interpretability layer derived from energy landscape frustration analysis. Both predictors exhibit a sharp and reproducible dichotomy on protein kinases, in which orthosteric ATP-binding sites can be identified with high precision, whereas allosteric sites are detected with substantially lower confidence across kinase structures and distinct conformational states. To decode this divergence, we deploy energy landscape-based explainable AI approach that integrates local frustration analysis as an independent physical interpretability layer, mapping predictive behavior to the underlying energetic organization of protein structures. This analysis reveals that predictive success is governed by the local energetic embedding of binding sites within the protein energy landscape. Orthosteric pockets are located in minimally frustrated basins that generate strong evolutionary and structural signatures, whereas allosteric pockets occupy predominantly neutrally frustrated zones associated with conformational plasticity and reduced evolutionary constraint. By integrating the prediction results with energy landscape analysis, our framework converts predictive performance into physically interpretable descriptors of binding site organization in protein kinases. These results establish energy landscape frustration as a potentially important determinant of algorithmic visibility and an interpretability layer providing a feasible strategy for diagnosing the limits of current prediction methods.
27 May 2026
Read appraisal →Zeitschrift fur Naturforschung. C, Journal of biosciences
Exploring the potency of 6-bromo-3-methylquinoline analogues as prostaglandin F2α inhibitors
Prostaglandin F2α (PGF2α) is associated with preterm labor and preterm birth. PGF2α inhibitors have thus proven to be a promising target in the development of lead compounds to prevent preterm birth. In this work, Quantitative Structural Activity Relationship (QSAR) was implemented on a dataset of 77 compounds of 6-bromo-3-methylquinoline analogues using statistical approach and random selection in the QSARINS software. The Genetic Algorithm-Multiple Linear Regression (GA-MLR) approach was used to predict the best model (R 2 = 0.8943 and Q 2 LOO = 0.8836). The inclusion of descriptors FNSA-2 and WV.mass resulted in a well-fitted and highly predictable model. Artificial neural network (ANN) analysis was also carried out to validate the model effectiveness. Twenty eight new molecules with better predicted biological activity (pIC50) were designed. The binding energy from the docking study of seven compounds have shown higher binding activity than P10 into prostaglandin F synthase protein (PDB ID: 2F38). The stability of protein-ligand complex was further validated by 100 ns molecular dynamics simulation and MM-PBSA binding free energy. DFT and ADME-toxicity analysis also confirmed their drug-likeness properties. Collectively, our findings highlight novel quinoline derivatives as promising lead candidates, warranting further validation through collaborative in vitro and in vivo studies.
27 May 2026
Read appraisal →Journal of computer-aided molecular design
In silico discovery of novel small-molecule PD-L1 inhibitors through a multi-stage computational workflow integrating machine learning and molecular dynamics
Cancer immunotherapy targeting the PD-1/PD-L1 pathway has transformed modern oncology; however, developing small-molecule inhibitors as viable alternatives to monoclonal antibodies remains a major challenge. In this study, an integrated computational framework is established in which machine learning, molecular docking, molecular dynamics simulations, and binding free-energy calculations are combined to enable the discovery and optimization of novel PD-L1 inhibitors. A validated ML-QSAR model was constructed using the XGBoost algorithm (R2_train = 0.925, R2_test = 0.743) on a dataset of 74 known inhibitors with consistent assay conditions. Through virtual screening of FDA-approved drugs, Pralatrexate was subsequently identified as a promising repurposing candidate, demonstrating a higher predicted binding affinity than the reference inhibitors. Structure-based modification of Pralatrexate yielded the derivative D1, which exhibited improved computational binding properties across all evaluation methods. Molecular dynamics simulations indicated that the D1-PD-L1 complex achieved greater stability than both the free protein and the reference complex, with reduced RMSD fluctuations and preserved key interactions with tyrosine residues Tyr56(A/B). MM-GBSA calculations further confirmed D1's superior binding affinity (-86.21 kcal/mol vs. - 73.65 kcal/mol for the reference), and predicted IC50 values suggested enhanced inhibitory potential. This multi-stage computational workflow effectively integrates machine learning predictions with atomic-level binding analyses, providing a robust platform for accelerated drug discovery. The optimized derivative D1 thus represents a promising candidate for experimental validation and further development as a potential cancer immunotherapeutic agent.
26 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Predictions from deep learning propose substantial protein-carbohydrate interplay
Noncovalent interaction between proteins and carbohydrates (sugars, glycans) is the basis for biological functions from metabolic regulation to intercellular recognition. It is a grand challenge to identify the protein-carbohydrate interactomes in organisms. Direct experiments would require extensive libraries of glycans to distinguish binding from nonbinding proteins. Computational screening of proteins for carbohydrate binding potential provides an attractive alternative. Current estimates propose that <5% of proteins bind carbohydrates, a number that is not well established. We therefore developed a neural network, "Protein interaction of Carbohydrates Predictor" (PiCAP), to predict whether a protein noncovalently binds to a carbohydrate. We trained PiCAP on a manually curated dataset of known carbohydrate binders and proteins that we identified as likely not to bind carbohydrates (transcription factors, cytoskeletal components, and small-molecule-binding proteins). PiCAP achieves 90% balanced accuracy on protein-level predictions of carbohydrate binding/nonbinding. Using the same datasets, we developed Carbohydrate Protein Site Identifier 2 (CAPSIF2) to predict protein residues that interact noncovalently with carbohydrates. CAPSIF2 achieves a Dice coefficient of 0.57 on residue-level predictions on our independent test dataset, outperforming previous models. To demonstrate the models' biological applicability, we investigated human cell surface proteins and further predicted the likelihood of carbohydrate binding in six proteomes (Escherichia coli, Mus musculus, Homo sapiens, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster). PiCAP predicts that ~35 to 40% of proteins in these proteomes bind carbohydrates, with 75% of extracellular and cell surface proteins predicted to bind. The PiCAP predicted binders are enriched for functions including growth factor receptor binding, inflammation, and cell-cell adhesion.
26 May 2026
Read appraisal →Cell reports methods
A tri-modal contrastive learning framework for protein representation learning
Protein foundation models, particularly protein language models, have shown strong success in learning meaningful protein representations using transformer architectures pretrained on large-scale datasets through self-supervised learning. These representations have proven effective for downstream tasks such as predicting protein functions and properties. However, most existing models focus solely on amino acid sequences, overlooking other informative modalities such as 3D structures and literature text. While some recent efforts incorporate multiple modalities, they often suffer from limitations in modality coverage or training strategy. To address this gap, we propose a multimodal pretraining framework that integrates three complementary modalities-protein sequences, structures, and literature text. Our method uses the sequence modality as an anchor and aligns the other two modalities to it via contrastive learning, enabling the model to capture richer and more holistic protein representations. Across a diverse set of downstream tasks, ProteinAligner outperforms state-of-the-art foundation models in predicting protein functions and properties.
20 May 2026
Read appraisal →Journal of molecular modeling
Exploring human GABA transporter 3 binders for epilepsy through quantum reactivity analysis, molecular dynamics, machine learning prediction, and network pharmacology
CONTEXT: Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, affecting millions of patients globally who do not respond to conventional antiepileptic medications. Recent evidence highlights GABA transporter 3 (GAT3), a glial subtype responsible for regulating extracellular GABA levels, as a promising molecular target for seizure control. Despite its therapeutic potential, selective and potent GAT3 inhibitors remain largely unexplored. This study aims to identify novel GAT3 inhibitors through an integrative computational strategy combining quantum chemical, molecular dynamics, and machine learning approaches to accelerate antiepileptic drug discovery. The cryo-EM-resolved human GAT3-SNAP-5114 complex structure was used for virtual screening, leading to the identification of three highest-ranking compounds, viz. Comp_1, Comp_2, and Comp_3, from the diverse chemically optimized screening library. Analysis of their quantum chemistry profiles indicated HOMO-LUMO energy gap values of 4.14 eV, 4.21 eV, and 3.26 eV, respectively, suggesting them to be maximally reactive with favorable electronic properties for interacting with biological molecules. Molecular dynamics simulations were performed in a fully hydrated POPC lipid bilayer using the AMBER force field, providing a physiologically relevant environment for assessing ligand binding and transporter dynamic, revealed stable binding modes, with the highest structural integrity or least deviations in the structure with Comp_2, having RMSD values ranging from 2.5 to 3.0 Å, with strong hydrogen bonding, while the binding mode was more flexible with strong anchorage for Comp_3, with its mode involving diverse interactions in the complex. Principal component and free energy landscape analyses identified energetically favorable, ligand-induced conformational states. Machine learning-based pIC₅₀ predictions using the AdaBoost regressor yielded values of 7.95 (Comp_1), 7.98 (Comp_2), and 7.63 (Comp_3), all comparable or superior to the reference inhibitor (7.89). A novel approach for network pharmacology integrating detailed drug-gene interaction profiling was additionally used to improve systems-level understanding of the discovered compound. These findings validate the potential of Comp_1-3 as lead GAT3-targeting antiepileptic agents. METHODS: A docking-based virtual screening of a structurally diverse compound library was performed against the cryo-EM structure of human GAT3 in complex with the reference inhibitor SNAP-5114. The top-scoring ligands were subjected to density functional theory (DFT) analysis to assess electronic properties such as HOMO-LUMO energy gaps, reflecting potential binding reactivity. Lead candidates were refined, docked, and evaluated through 500 ns molecular dynamics (MD) simulations, analyzing structural stability via RMSD, RMSF, hydrogen bonding, and interaction network profiles. Principal component analysis and free energy landscape evaluations were used to explore ligand-induced conformational dynamics. Furthermore, machine learning (ML) models-particularly the AdaBoost algorithm-were employed to predict pIC₅₀ values, confirming bioactivity trends. Finally, network pharmacology analysis was conducted to elucidate gene targets and pathway interconnections, revealing the multi-pathway involvement of top candidates in seizure regulation. This integrative computational framework underscores the potential of GAT3-focused modelling and ML-guided screening for rational antiepileptic drug design.
14 May 2026
Read appraisal →Journal of computer-aided molecular design
Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights
Tuberculosis (TB) remains a major global health challenge driven by persistent Mycobacterium tuberculosis infection and increasing drug resistance. Phytochemicals represent a structurally diverse and underexplored chemical space for anti-TB drug discovery, yet systematic prioritization strategies integrating machine learning and structure-based validation are limited. A curated phenotypic anti-TB dataset of 425,180 compounds was used to train ensemble ExtraTrees models based on ECFP4 fingerprints and physicochemical descriptors. The models achieved strong predictive performance (ROC-AUC up to 0.983; MCC up to 0.871). SHAP analysis enabled mechanistic interpretation by identifying the key molecular descriptors and fingerprint features driving anti-TB activity predictions. The validated ensemble was applied to screen 4707 phytochemicals, yielding 3209 predicted actives, of which 778 satisfied applicability domain criteria. High-confidence candidates were subsequently evaluated by molecular docking against twelve structurally validated essential M. tuberculosis targets spanning cell wall biosynthesis, energy metabolism, nucleotide synthesis, and cofactor pathways. Docking analysis identified 486 phytochemicals with favorable predicted binding affinities, including 193 compounds exhibiting multi-target engagement. Several top-ranked candidates reproduced canonical interaction patterns of co-crystallized inhibitors, supporting mechanistic plausibility. This integrated chemoinformatics and structure-based framework enables robust prioritization of phytochemicals with biologically meaningful and multi-target antitubercular potential. The study provides a computationally grounded strategy for accelerating lead identification against drug-resistant TB.
13 May 2026
Read appraisal →Journal of chemical information and modeling
MechMemDyn: Coupling Frustration Analysis with Membrane Dynamics to Target the TREM2-DAP12 Complex Interface
The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes a pivotal therapeutic target for Alzheimer's disease. However, the intrinsic plasticity of its interface poses a formidable challenge for structure-based discovery of protein-protein interaction stabilizers (PPI stabilizers). Conventional approaches, ranging from static molecular docking to geometric deep learning, fail to capture the subtle interfacial energetics required for stabilizing this assembly, often leading to erroneous activity predictions. Here, we present MechMemDyn, a novel predictive framework that uniquely integrates protein frustration analysis with membrane-embedded molecular dynamics (MD) simulations. To our knowledge, this is the first systematic application of frustration analysis to rationalize PPI-stabilizing activity and guide ligand design. By mapping the local frustration landscape, we identify critical, minimally frustrated contact networks at the protein interface, which are essential for stabilizing the PPI. We demonstrate that the ability of a ligand to dampen distance fluctuations within these key networks, a metric rooted in thermodynamic rigor, correlates strongly with experimental potency. This method outperforms conventional static docking, AI-driven dynamic docking approaches, and standard molecular simulations, providing a more accurate and reproducible basis for cross-ligand comparison. This work not only resolves the intractability of the TREM2-DAP12 complex but also establishes a physics-driven paradigm for targeting dynamic transmembrane interfaces via frustration-optimized PPI stabilizers.
13 May 2026
Read appraisal →Journal of chemical information and modeling
A Transformer for Reaction-Aware Compound Explorations with GFlowNet in QSAR-Guided Molecular Design
In drug discovery tasks, achieving a balance between high biological activity toward therapeutic targets and synthetic chemical feasibility is critically important. While the recently proposed deep learning-based molecular generation models have enabled explorations of vast chemical spaces, most existing approaches do not consider synthetic routes for generated compounds. To address this issue, TRACE-GFN is proposed for molecular optimization; this method incorporates chemical reaction pathways into a quantitative structure-activity relationship (QSAR)-guided molecular design procedure. The method integrates a transformer model to explicitly learn chemical reactions with a generative flow network (GFlowNet) that efficiently samples diverse candidates. In benchmark experiments involving dopamine receptor D2 (DRD2), AKT serine/threonine kinase 1 (AKT1), and C-X-C motif chemokine receptor 4 (CXCR4), TRACE-GFN demonstrated the ability to identify compounds with high QSAR values while maintaining strong diversity, outperforming the existing molecular generation models. These results demonstrate that the proposed model can efficiently explore promising compounds while accounting for real-world chemical reactions. The source code is publicly available under an MIT license at https://github.com/sekijima-lab/TRACE-GFN.
13 May 2026
Read appraisal →Journal of chemical information and modeling
Artificial Intelligence for Predicting Small-Molecule Bioactive Conformations
Precise prediction of bioactive conformations represents a central challenge in drug discovery. Traditional computational methods often rely on the assumption that the global minimum energy state corresponds to the bioactive conformation─a hypothesis with inherent limitations. Data-driven artificial intelligence (AI) models have revolutionized research methodologies in this domain. By learning the implicit probability distributions of conformations, AI demonstrates the potential to achieve high-precision predictions of bioactive structures. This review systematically surveys technical advancements ranging from geometric regression to generative and conditional generative models, analyzing their effectiveness in benchmarks and de novo design experiments. We deeply explore critical challenges, including data sparsity, sampling efficiency, generation capability, and interpretability. Finally, we provide an outlook on bridging the gap from "structural prediction" to "functional design" through the construction of dynamic data sets, the development of efficient algorithms, and the implementation of human-in-the-loop collaborative decision-making systems.
13 May 2026
Read appraisal →Journal of computer-aided molecular design
NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network
Neuropeptides are multifunctional signaling molecules in the nervous system. By modulating synaptic transmission and integrating physiological systems, they influence a broad range of functions from pain perception to emotional regulation. Predicting neuropeptides can rapidly expand the library of potential therapeutic targets, thereby providing novel candidate molecules for drug development in areas such as analgesics, anti-anxiety medications, and weight-loss drugs. Traditional experimental methods are extremely time-consuming, labor-intensive, these promising alternative computational methods have emerged. In this study, a dual-branch deep learning architecture for neuropeptide prediction known as NeuroPred-GMC are built up based on gated dilated convolutional network with ESM-2 feature representation and multi-scale convolutional network with Prot-T5 feature representation. Dilated convolution exponentially enlarges the receptive field via increased dilation rates, gating mechanism enables dynamic, selective feature enhancement and noise suppression, and multi-scale convolution captures multi-level contextual information. On the independence test set, the accuracy of 93.24%, Sn of 93.69%, Sp of 92.79%, Pre of 92.86%, MCC of 0.8649 and the auROC of 0.9667 are obtained. The experimental results through cross-validation and independent test demonstrate that the proposed model has good robustness and generalizability, and can serve as a supplemental candidate predictor. The source datasets and codes can be freely available at https://github.com/yunyunliang88/NeuroPred-GMC .
11 May 2026
Read appraisal →European journal of medicinal chemistry
AI-driven identification of a selective dual function inhibitor blocking HK2 activity and HK2-VDAC1 interaction displaying enhanced anticancer efficacy under hypoxia
Selective inhibition of hexokinase 2 (HK2) represents a promising therapeutic strategy due to the pivotal role of HK2 in the Warburg effect, enhancement of glycolysis and anti-apoptosis via HK2-Voltage-Dependent Anion Channel 1 (VDAC1) protein-protein interaction. Moreover, HK2 initiates glycolysis to generate lactate, hence this central enzyme can be pharmacologically targeted to enhance therapy outcomes. Currently, no HK2 inhibitors (HK2is) exist in the clinic. Herein, we employed GCVec, an artificial intelligence (AI)-based compound-protein interaction (CPI) prediction tool, along with molecular docking, to identify the HK2i, 106. This compound exhibited an IC50 of 0.79 ± 0.07 μM and a consistent Kd of 0.41 ± 0.03 μM against HK2 enzyme. It also apparently blocked HK2-VDAC1 interaction as indicated by the disrupted colocalization of HK2-GFP and VDAC1-mCherry. Furthermore, 106 demonstrated enhanced anticancer efficacy under hypoxia in tumor cells with elevated HIF-1α/HK2 and VDAC1 levels. Compound 106 selectively targeted SW480 colorectal cancer cells with high HK2 expression, achieving a growth inhibition IC50 value of 5.00 ± 0.94 μM. Consistently, knockout of HK2 in these tumor cells significantly rescued the IC50 values and eliminated the glycolytic inhibition induced by 106. We further showed that 106 reduced lactate and ATP levels and induced markers of apoptosis, including increased p-AMPK/AMPK ratio and increased Bax levels, as well as decreased Bcl2 levels. Collectively, our findings highlight the potential of GCVec in identifying 106, a first in class dual-function HK2i which emerges as a promising lead compound for further development into a possible anticancer therapeutic agent.
11 May 2026
Read appraisal →Briefings in bioinformatics
Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction
Drug discovery is a time-consuming, expensive, and high-risk process. Recent advances in artificial intelligence (AI) have enabled major breakthroughs in small-molecule and protein therapeutics. However, AI-driven design of aptamer drugs remains largely unexplored. Aptamers are short (15-100 nt) single-stranded DNAs or RNAs that exhibit high binding affinity, high specificity, and low immunogenicity, making them promising candidates for disease (such as cancer) therapeutics. Compared with protein-ligand or protein-protein systems, protein-aptamer complexes are under-represented in public structural databases, and aptamers themselves are highly flexible and relatively large molecules. These characteristics present distinct challenges for AI-based structural modeling. Here, we systematically evaluate recent AI frameworks, including AlphaFold3, Chai-1, Boltz-2, and RoseTTAFold2NA, along with a template-based approach, in predicting protein-aptamer complex structures and estimating binding free energies. We establish an independent benchmark to assess their performance in structural accuracy, stability, and energetic consistency. This study provides a foundation for the application of AI in aptamer drug design and offers a reference framework for future research in nucleic-acid therapeutics and biomolecular modeling.
10 May 2026
Read appraisal →Computers in biology and medicine
A decoction-inspired genetic algorithm and PPO reinforcement learning for intelligent molecular discovery: Anti-colorectal cancer candidates from the tiao-pi AnChang formula
BACKGROUND: Colorectal cancer (CRC) remains among the leading causes of cancer-related mortality worldwide. The TiaoPi AnChang Decoction (TPACD), a traditional Chinese herbal formula, has shown potential anti-CRC activity in preclinical studies (in vitro and in vivo); however, its active chemical basis and key target interactions remain unclear. The complex, multicomponent nature of TPACD poses significant challenges for systematically identifying and optimizing compounds. PURPOSE: This study aimed to establish an intelligent molecular discovery framework that integrates a decoction-inspired genetic algorithm (EGA) with proximal policy optimization (PPO)-based reinforcement learning to simulate the dynamic transformation and recombination processes underlying traditional herbal decoctions and to generate and prioritize in silico candidate molecules for further anti-CRC evaluation. STUDY DESIGN: A parallel molecular generation strategy was developed, comprising three algorithmic pathways, namely, the basic molecular generation algorithm (BMGA), enhanced molecular generation algorithm (EMGA), and intelligent molecular generation algorithm (IMGA), which represent progressive levels of structural diversity, biological relevance, and optimization capacity. METHODS: The EGA simulated the selection-transformation-recombination principles of decoction through adaptive mutation, fragment recombination, and a diversity-preserving selection procedure. PPO-based reinforcement learning further refined the molecular properties via a reward-guided exploration of the chemical space. The BMGA enabled baseline molecular generation; the EMGA incorporated affinity-based selection; and the IMGA achieved multiobjective optimization, integrating drug likeness, novelty, and toxicity filtering. The candidate molecules were validated by affinity prediction, QED scoring, and molecular docking. RESULTS: All three methods generated chemically valid and pharmacologically enhanced molecules relative to the original TPACD components. The EMGA provided improved biological affinity and exploration efficiency, whereas the IMGA achieved the highest overall performance level, with superior QED and docking stability, and predicted the CRC inhibition potential of the molecules. CONCLUSION: The proposed EGA-PPO framework effectively connects traditional decoction principles with modern AI-based drug design. The tri-algorithmic system (BMGA-EMGA-IMGA) provides a scalable and interpretable strategy for de novo molecular discovery, offering promising leads for acquiring natural product-derived CRC therapeutics.
9 May 2026
Read appraisal →Journal of molecular graphics & modelling
DeepHybridCPI: A hybrid deep learning framework for compound-protein interaction prediction
In bioinformatics, deep learning-based methods for Compound-Protein Interaction (CPI) prediction play a vital role in virtual screening, drug discovery, and drug repositioning. Recent improvements in computational methods have shown great possibility to save costs of experiment and speed up target identification. Nevertheless, the current CPI forecasting methods remain severely limited. Many rely on shallow Graph Neural Networks (GNNs) that struggle to capture the global structural context of compounds, while conventional Convolutional Neural Networks (CNNs) focus primarily on local sequence motifs and fail to model long-range dependencies in proteins. Even though a number of recent architectures strive to solve these problems by adding complexity to models, or by adding complex modules, these additions often cause significant computational overhead. To overcome these challenges, we propose DeepHybridCPI, a hybrid deep learning framework designed for accurate and efficient CPI prediction. Our hybrid model integrates a multiscale, densely connected GNN to extract compound features capturing both local substructures and global molecular topology, and employs CNNs with Long Short-Term Memory (LSTM) networks to model both local motifs and extended dependencies in protein sequences. The learned compound and protein representations are fused into a unified latent space to enable effective interaction modeling. Experimental evaluations on benchmark Human and C. elegans datasets demonstrate that DeepHybridCPI consistently outperforms existing state-of-the-art baseline methods in terms of AUC, Precision, and Recall. These findings highlight the importance of combining multiscale compound representations with hybrid sequence encoders within a single unified framework, providing a promising avenue for accelerating computational drug discovery. We release our source code and dataset at: https://github.com/jamshaidwarraich/DeepHybridCPI.
4 May 2026
Read appraisal →Molecular informatics
ADME-DTI: Augmented Deep Meta Ensemble for Drug-Target Interaction Prediction
Drug-target interaction represents a critical focus area in computational drug discovery and pharmaceutical research. However, the process of identifying and analyzing these interactions is often resource-intensive, requiring extensive experimentation to evaluate the binding relationships between numerous drugs and their respective targets. This complexity is further compounded by the fact that a drug can inhibit multiple targets, and a target may also bind to various drugs. To address these issues, advanced deep learning models have been introduced as promising tools, offering the ability to accurately predict binding affinity and other bioactivity values to distinguish between potential drug-target interactions. The proposed model, named the Augmented Deep Meta Ensemble for Drug-Target Interaction (ADME-DTI), leverages multiple descriptors and fingerprint representations to extract meaningful insights from drug and protein data. These submodels from each representation are then combined with a deep learning architecture along with the metadata of the drug and target entries. The proposed approach has proved competitive performance against state-of-the-art models across diverse datasets and evaluation metrics, as evidenced by key metrics, namely r m 2 $r_m^2$ , concordance index, and mean squared error. Specifically, the model achieved mean squared error values of 0.186 (Davis), 0.118 (Kiba), 0.134 (DTC), 0.262 (Metz), 0.300 (ToxCast), and 0.791 (STITCH), all of which are publicly available benchmark datasets commonly used in drug-target interaction prediction tasks. The results highlight the model's effectiveness in reducing prediction errors and improving accuracy. Previous drug-target interaction research often relied on limited, non-diverse datasets, reducing generalizability. Few studies addressed drug-target interaction prediction as a regression task. Our novel deep learning metamodel integrates multiple models and representations, surpassing benchmarks and delivering greater prediction accuracy across varied datasets and evaluation metrics.
4 May 2026
Read appraisal →Journal of computer-aided molecular design
Resolving the ambiguous binding site of quercetin at the calcineurin subunit junction using funnel metadynamics with deep learning collective variables
Calcineurin represents a prominent target for immunosuppressive drugs, where conventional macrocyclic inhibitors utilize an immunophilin-dependent mechanism for their inhibition but are consequently hindered by adverse effects and variable pharmacokinetics. The flavonoid quercetin has been shown to inhibit calcineurin in a non-competitive, immunophilin-independent manner, but its exact binding site remains ambiguous at the junction between calcineurin subunits A and B, with the open solvent-exposed nature of this region proving challenging to model. In this study, we employ funnel metadynamics with collective variables derived from a deep learning model to identify the binding site of quercetin. A selective strategy using multiple simulations with stricter funnel definitions and a smaller number of carefully chosen descriptors for model training proved more effective than a broad-based approach. These simulations were able to effectively distinguish the binding site of quercetin from three experimentally suggested sites, with a calculated free energy of binding of -8.36 ± 0.60 kcal/mol showing excellent agreement with experiment. Ligand-tryptophan distances similarly corroborated measurements from FRET assays with a r2 of 0.85. The corresponding binding pose showed that quercetin inserts itself into a channel between Arg122, Gly123, Tyr124 on one side and Phe160, Thr161, and Asn345 on the other, stabilizing its binding through a network of hydrogen bonds. The findings of this study provide insights into the modelling of challenging binding sites and ligands using deep learning driven metadynamics simulations and provide the foundations for rational development of immunophilin-independent inhibitors of calcineurin.
3 May 2026
Read appraisal →ChemMedChem
A Novel Triose Phosphate Isomerase Inhibitor With Dual Trypanosomicidal Activity was Identified Using Artificial Intelligence-Based Virtual Screening
Chagas disease and leishmaniasis are neglected protozoan diseases recognized by the World Health Organization as major public health problems. These diseases affect millions of people worldwide, yet effective treatments remain unavailable. Triosephosphate isomerase (TIM), a glycolytic enzyme that exhibits high catalytic efficiency for the isomerization of glyceraldehyde-3-phosphate and dihydroxyacetone-phosphate exclusively in its dimeric form, was subjected to virtual screening. Using a deep neural network for structure-based drug design that predicts binding affinity between small molecules and proteins of known structure, 12.5 million commercially available compounds were screened. From this, 82 compounds were selected for in vitro evaluation. Six compounds inhibited TIM from Trypanosoma cruzi, three of which exhibited anti-T. cruzi activity. Eight compounds demonstrated activity against the parasites T. cruzi and Leishmania infantum. Two compounds showed similar potency against both parasites: 3-(1-acetyl-5-(4-bromophenyl)-4,5-dihydro-1H-pyrazol-3-yl)-4-hydroxy-6-methyl-2H-pyran-2-one (IC50 = 16 ± 3 μM) and 3-[(4-bromophenyl)sulfanyl]-1-(3-nitrophenyl)propan-1-one (IC50 = 12 ± 1 μM). These compounds exhibit favorable selectivity and toxicological profiles, as well as in vivo activity, indicating their potential for future drug development.
2 May 2026
Read appraisal →Journal of chemical information and modeling
Generative AI Uncovers Novel Chrebp/Txnip Axis Inhibitors with Potential Anti-inflammatory Activity
Type 2 diabetes is driven in part by metabolic inflammation, where activation of the Chrebp/Txnip axis promotes NLRP3 inflammasome assembly, leading to pancreatic β-cell dysfunction and pro-inflammatory cytokine release. Despite the therapeutic relevance of this pathway, the Chrebp-14-3-3 (regulatory-protein client) protein-protein interaction (PPI) remains largely underexplored, with only a limited number of small-molecule modulators reported to date. To address this gap, we developed an artificial intelligence-driven generative design framework for de novo discovery of selective PPI-targeting compounds. A conditional recurrent neural network (cRNN), implemented as a quantitative structure-property relationship-guided generative network (QSPR-GEN), was pretrained on a large, chemically diverse corpus to learn general SMILES syntax and structural priors, and subsequently fine-tuned on a curated, target-focused data set of approximately 5900 compounds, achieving high scaffold uniqueness (94.6%). Selectivity-oriented physicochemical descriptors were incorporated as conditional inputs to bias generation away from promiscuous chemotypes, while maintaining anchoring to a known active seed. Structure-based refinement was further applied by focusing on the noncanonical α-helical epitope unique to the Chrebp regulatory-protein interface, establishing a dual-layered strategy for selective PPI modulation. The integrated pipeline, combining virtual screening, molecular dynamics simulations, and MM/PBSA free-energy calculations, prioritized lead candidates with favorable binding energetics and pharmacokinetic profiles. In THP-1 macrophages under metabolic stress, the top candidate T7 markedly suppressed Txnip and NLRP3 expression, reduced IL-1β secretion, and attenuated pyroptotic cell death, outperforming a reference inhibitor. Collectively, this study presents a robust computational framework for the inverse design of challenging PPIs and demonstrates its utility through the identification and experimental validation of mechanistically precise lead compounds, exemplified by T2 and T7.
2 May 2026
Read appraisal →Journal of chemical information and modeling
Explainability Methods from Machine Learning Detect Important Drugs' Atoms in Drug-Target Interactions
Predicting drug-target interactions (DTI) with graph neural networks (GNNs) is hindered by their lack of interpretability. To address this, we benchmark four explainable artificial intelligence (XAI) attribution methods on GNN models trained for kinase and G-protein-coupled receptors (GPCR) targets. We assess the methods' consistency through atom-level intersection over union (IoU) and validate their biological relevance by mapping attributed atoms to three-dimensional (3D) protein-ligand structures. While consistency across methods was modest, consensus attributions were highly enriched for atoms directly contacting the binding pocket─up to 76% within 2 Å in the kinase-inhibitor complexes. Notably, these attributed atoms were frequently found contacting experimentally important regulatory residues such as those in the DFG motif. This indicates that XAI methods, despite their disagreements, can identify chemically meaningful ligand features, providing a foundation for developing more interpretable GNNs in drug discovery.
2 May 2026
Read appraisal →Journal of chemical information and modeling
A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations
Achieving optimal target activity while maintaining synthetic accessibility and drug-likeness represents a major challenge in computational drug discovery. Existing de novo generative models often yield chemically invalid or synthetically intractable structures and struggle to optimize multiple objectives simultaneously. Here, we introduce ALCHIMIA, an interpretable hybrid framework combining reinforcement learning (RL) and a genetic algorithm (GA), built based on a vocabulary of 33 medicinal chemistry-inspired molecular transformations. The RL component trains a policy network to prioritize transformation sequences that improve synthetic accessibility (SA) and the quantitative estimate of drug-likeness (QED) scores, embedding these constraints directly into molecular generation. The GA component applies the learned policy as a mutational operator within population-based optimization guided by molecular docking, enabling the exploration of diverse chemical lineages while converging toward high-affinity ligands. ALCHIMIA was applied to two different pharmacologically relevant targets: human Cannabinoid Receptor 2 (CB2R) and human Sigma nonopioid intracellular Receptor 1 (S1R). We considered three different scenarios: (i) unconstrained hit identification; (ii) scaffold-constrained lead optimization; and (iii) design of dual modulators. The framework generated chemically valid molecules with QED and SA scores comparable to or better than those obtained with random baselines and selected de novo design methods. By codifying typical medicinal chemistry actions as learnable transformations and coupling multiobjective optimization with GA-based diversity maintenance, ALCHIMIA, freely available as a GitHub repository (https://github.com/alberdom88/ALCHIMIA), provides a practical, interpretable, and scalable framework for molecular de novo design.
2 May 2026
Read appraisal →Journal of ethnopharmacology
MAGED: Multimodal attentive graph learning with gene expression dynamics on knowledge graphs for TCM target prediction
ETHNOPHARMACOLOGICAL RELEVANCE: Plants used in traditional medicine have long provided valuable clues for drug discovery, yet systematically connecting their documented therapeutic effects with modern understanding of biological mechanisms remains a key scientific question. Bridging the gap between traditional knowledge, clinical symptom profiles, and contemporary molecular-level regulation is essential for advancing the study of herbal medicine. OBJECTIVES: This study aims to develop a multimodal graph attention learning framework, named MAGED, which integrates knowledge graphs with gene expression dynamics to improve the accuracy and interpretability of Herb-Target Interaction (HTI) prediction. METHODS: MAGED incorporates multi-source heterogeneous data, including traditional Chinese medicine (TCM) properties, hierarchical clinical symptom information, and molecular-level biological data, such as gene expression dynamics under drug intervention and causal biological networks. A multimodal fusion encoder is employed to embed herb attributes into efficacy-related feature vectors, which are then combined with functional representations (e.g., normalized enrichment scores) to form a unified contextualized representation. Additionally, a hierarchical graph attention network is designed to integrate macroscopic symptom-gene associations with microscopic regulatory pathways, thereby establishing a coherent inference chain from herbal effects to biological targets and phenotypic symptoms. RESULTS: Systematic experimental evaluations demonstrated that MAGED significantly outperforms existing baseline methods across multiple evaluation metrics, achieving a 59.8 % improvement in HR@10. The model also exhibited superior ranking and recall performance under cold-start scenarios. In a case study on Scutellaria baicalensis, 8 out of the top 10 predicted targets were supported by existing literature, with functional or direct interaction evidence, and some targets were further validated through molecular docking. CONCLUSION: MAGED provides an accurate and interpretable framework for predicting herb-target interactions, effectively integrating traditional knowledge with modern molecular evidence. This approach shows strong potential to facilitate the discovery of herbal mechanisms and the identification of novel therapeutic targets.
27 Apr 2026
Read appraisal →Journal of medicinal chemistry
Machine Learning Approach to Anticancer Activity Prediction of Transition-Metal Complexes Based on a Large-Scale Experimental Database
In this work, we developed a straightforward data-driven approach to predict the cytotoxicity of metal complexes based entirely on their (metal + ligands) composition. To this end, we have manually curated MetalCytoToxDB─a comprehensive experimental database comprising 26,500 IC50 values for 7050 metal complexes against 754 cell lines from 1921 articles. Based on these, machine learning models were created to accurately assign the cytotoxicity class within the ruthenium and iridium subsets. Moreover, external validation of the best-performing model on the unseen data was carried out. The possibility of multimetal predictions was explored, enabling assessment of cytotoxicity among the complexes of metals, for which experimental data are relatively scarce. The interpretability and limitations of the developed models are discussed. Finally, a pipeline for the effective high-throughput computational screening of ruthenium complexes is proposed. The MetalCytoToxDB is available online for AI-assisted exploration at https://biometaldb.streamlit.app/.
25 Apr 2026
Read appraisal →Cell systems
Context-informed subgraph foundation models enable interpretable protein-function prediction
Protein-function prediction is crucial for elucidating molecular mechanisms driving biological processes and therapeutics development. Despite numerous computational tools demonstrating promising performance, they fall short when predicting rare, uncharacterized functions or indirect activities. Here, we present COSMOS, a context-aware Gene Ontology (GO) subgraph mining system for protein-function prediction. By leveraging inductive subgraph foundation models and an enriched knowledge graph of protein-GO relationships, COSMOS performs zero-shot, few-shot, and low-homology protein-function prediction. Built on 7,923,952 functional semantic relationships, COSMOS demonstrates robust capabilities to (1) generate state-of-the-art predictions for GO classes with sparse or no experimental annotations, (2) provide interpretable functional subgraphs for transparent rationale analysis, and (3) deliver complementary benefits when integrated with existing embedding-based prediction methods. We anticipate that COSMOS will serve as a complementary approach to conventional protein annotation methods and an interpretable tool for predicting protein functions within underexplored GO classes, thereby advancing genomics and therapeutic research.
18 Apr 2026
Read appraisal →Journal of molecular modeling
HopWD-DTA: a novel framework for drug-target affinity prediction fusing multi-hop neighborhoods and deep features
CONTEXT: Accurate prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery, but it remains a significant challenge. While deep learning methods have shown promise, many existing models struggle with representing long-range protein relationships within protein-protein interaction (PPI) networks, often suffering from performance degradation as graph neural network layers deepen. To overcome this limitation, we developed HopWD-DTA, a novel framework designed to effectively capture both local and global protein features. The core innovation lies in integrating multi-hop neighborhood information from PPI networks with deep structural features of proteins and drugs. Comprehensive evaluations on the Davis, KIBA, and Human benchmark datasets demonstrate that HopWD-DTA achieves state-of-the-art performance, significantly improving DTA prediction accuracy over existing cutting-edge solutions. METHODS: The HopWD-DTA framework consists of separate branches for protein and drug feature extraction. For proteins, structural features from residue contact maps are first encoded using a Graph Convolutional Network (GCN). These features, along with InterPro annotations, are then embedded into a PPI network. We introduce a multi-hop neighborhood serialization technique, which generates a sequence of feature matrices representing different neighborhood scopes. This sequence is processed by a Variational Autoencoder (VAE) to learn a robust protein representation. For drugs, molecules are represented as graphs from SMILES strings and encoded via a GCN, followed by a novel Wide-and-Deep Path (WDPATH) module to capture both macroscopic and microscopic features. The final protein and drug features are concatenated and fed into a multilayer perceptron for affinity prediction. The model was implemented using PyTorch and RDKit.
15 Apr 2026
Read appraisal →Journal of chemical information and modeling
AI-MedCraft: A Strategy-Driven AI Platform for Multi-Objective Molecular Design
Small-molecule drug discovery requires balancing potency, selectivity, solubility, safety, and synthetic feasibility, among other interdependent properties. Yet most computational workflows address these properties sequentially or collapse them into fixed weighted scores. Although artificial intelligence has accelerated molecular design, many platforms struggle to handle explicit multiobjective trade-offs. Here, we present AI-MedCraft, a strategy-driven molecular design framework that applies adaptive, Pareto-guided reinforcement learning to optimize multiple objectives concurrently within a unified workflow. When structural information is available, physics-aware scoring is incorporated to support binding-competent designs. In a structure-based benchmark on the solubility rescue of the BTK inhibitor GDC-0834, AI-MedCraft achieves broader Pareto-front coverage than the scalar-reward reinforcement learning framework REINVENT 4 under matched objectives and computational cost. In a second case study, AI-MedCraft redesigns Efavirenz to retain HIV-1 reverse transcriptase engagement while reducing predicted 5-HT2A off-target binding, demonstrating its use in multiconstraint molecular optimization.
14 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks
Many protein-protein interactions (PPIs) are mediated by the binding of short linear motifs (SLiMs) to peptide recognition domains (PRDs). Here, we describe PrePPI-SLiM, a proteome-scale computational pipeline that leverages data from the Eukaryotic Linear Motif (ELM) database to predict whether two proteins will form a peptide-mediated complex. The ELM database defines classes of protein-peptide interactions with SLiMs represented by sequence motifs and PRDs represented by Pfam domains. PrePPI-SLiM systematically evaluates all pairwise combinations of proteins within a proteome and identifies PRD-SLiM pairs that occur in the same ELM class. This evidence together with disorder prediction and sequence conservation of the motif are integrated in a naïve Bayes framework to assign a likelihood for complex formation. To obtain potential PDB templates for atomistic models of PrePPI-SLiM interactions, we associate individual PPI predictions with homologous PDB complexes involving the same PRD Pfam domain and SLIM, and obtain PDB templates for 92% of our high-confidence predictions. Moreover, studies with AF3Complex suggest that prior knowledge of the interacting PRD and SLiM, as provided here, is a critical starting point for creating a 3D model of the specific sequences of the PRD and SLiM query proteins. Finally, we demonstrate that clustering of the high-confidence PrePPI-SLiM interactome yields functionally coherent PPI networks that reveal mechanistic insights into cellular processes. The PrePPI webserver provides convenient access to high-confidence PrePPI-SLiM predictions, PDB templates for modeling, and functional networks.
8 Apr 2026
Read appraisal →Journal of computer-aided molecular design
Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach
Cyclin-dependent kinase 16 (CDK16), a serine/threonine protein kinase, is a critical regulator of cell cycle progression, vesicle trafficking, and apoptosis. Its dysregulation is implicated in the progression of aggressive cancers, including triple-negative breast, lung, and prostate cancer, where its overexpression correlates with poor prognosis. Despite its therapeutic promise, CDK16 remains an understudied kinase lacking selective inhibitors, underscoring the need for innovative discovery approaches. This study introduces a novel computational framework that leverages multiple docked poses to augment datasets for regression-based machine learning (ML) models targeting CDK16 inhibition. This data augmentation strategy incorporated docking scores, ligand-receptor contact fingerprints (LRCFs), and conformation-sensitive physicochemical descriptors as input features. To our knowledge, this is the first application of docked pose augmentation for a regression-based drug discovery model. A systematic evaluation of eight ML algorithms across multiple docking score consensus levels identified Gradient Boosted Trees as the optimal learner. The Genetic Function Algorithm was integrated to select a minimal set of descriptors, which improved model generalizability. The resulting validated ML-QSAR model guided the generation of a robust pharmacophore model, which was used for virtual screening of the NCI and OpnMe databases. Subsequent in vitro LanthaScreen kinase assays confirmed two novel and potent CDK16 inhibitors: compound H_28 (OpnMe code: BI-831266) with an IC50 of 3.5 µM, and H_33 (OpnMe code: BI-1282) with an IC50 of 5.8 µM. These findings highlight the efficacy of integrating docked pose augmentation with regression modeling and experimental validation, offering a robust framework for accelerating the discovery of targeted anticancer agents.
7 Apr 2026
Read appraisal →Journal of computer-aided molecular design
Identification of potential inhibitors of 3‑mercaptopyruvate sulfurtransferase with a deep-learning based screening of natural products
3-Mercaptopyruvate sulfurtransferase (3-MST), a key enzyme in sulfur metabolism, has recently gained attention as a potential anticancer target. However, reported 3-MST inhibitors remain limited, motivating the exploration of new scaffolds such as natural products. In this study, a library of 3744 natural products was virtually screened against human 3-MST using DiffDock (diffusion-model-based docking) followed by AutoDock Vina docking. Top-ranking candidates were further analyzed via molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann Surface Area binding free energy calculations. Methylophiopogonanone A (4), Daphnoretin (5), and L-asarinin (9) exhibited stable binding with favorable energetics, displaying binding free energies comparable to the reference ligand 7NC301. Binding mode analyses revealed that Methylophiopogonanone A primarily engaged in hydrophobic interactions, whereas Daphnoretin and L-asarinin formed extensive polar contacts, accompanied by higher desolvation penalties. In vitro cytotoxicity assays showed that Methylophiopogonanone A and L-asarinin reduced HCT116 cell viability by 40.3% and 26.3% at 25 µM, which is consistent with their inhibitory of 3-MST with IC50 = 5.83 ± 0.69 µM and IC50 = 19.11 ± 3.37 µM, respectively. These results suggested that the natural products identified in this study represent promising scaffolds for further optimization as potential 3-MST inhibitors. This work provides an AI-guided natural-product screening workflow for 3-MST and delivers prioritized inhibitor scaffolds for subsequent optimization and experimental validation.
6 Apr 2026
Read appraisal →Cell
A generative deep learning approach to de novo antibiotic design
The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders. Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection. We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space.
1 Oct 2025
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