Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Journal of computer-aided molecular design
Artificial intelligence in impurity prediction: current landscape, challenges, and future directions
Earlier identification, regulation of impurities in pharmaceutical products is critical throughout the medication development process because they have a high impact on drug quality, safety, and regulatory approval. Traditional analytical methods like LC-MS and NMR are frequently used, but they are labour intensive, time consuming and have limited capacity to detect unknown or trace level impurities. New opportunities to forecast impurity formation before experimental observation have been enabled by developments in cheminformatics and computer modelling. This study covers advanced chemical representation techniques, data generation, data curation, and model validation with highlighting recent advances in machine learning and deep learning algorithms for impurity prediction. In association with forecasting synthetic by products, degradation products, and harmful contaminants, traditional QSAR techniques, and contemporary deep learning models such as graph neural networks, transformer based architectures, and generative frameworks are examined. Additionally, discussed the increasing significance of explainable models for regulatory body acceptability. Lastly, newly developed advanced techniques like digital twins, automated impurity profiling, integrated reaction degradation modelling, and real-time monitoring are examined, demonstrating how computational methods are transforming impurity assessment from a reactive effort into a predictive and preventive procedure.
23 July 2026
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PegaPlus─Interactive Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure-Activity Data
In the early stages of drug discovery, the identification of molecular series with lead-like properties is essential for structure-activity relationship studies. A common first step is to cluster data derived from high-throughput screening (HTS). This data then needs to be exhaustively refined by an expert to identify bioactive compound classes. Here, we present the novel, interactive, and visual approach named PegaPlus for the analysis of HTS data, facilitating the incorporation of expert knowledge with the strategy of learning by observation. PegaPlus uses a tailored variant of a stochastic proximity embedding algorithm to visualize the clustered data in a two-dimensional plane via a web interface. The user can iteratively modify the proposed compound clustering by dragging data points in the drawing plane. At each step, an online support vector machine learns and improves its clustering predictions by incorporating user modifications and the molecular features of the compounds. The visualization is also updated based on the user's input and molecular similarity. PegaPlus was developed with the exploratory process in mind and allows the user to freely organize molecules on the drawing plane, or filter them by various molecular properties. Our evaluation shows that PegaPlus reduces the number of refinement steps needed by half in comparison to the purely manual approach on multiple data sets. These results demonstrate how an interactive machine learning approach can reliably support medicinal chemists and automate time-consuming, manual refinement tasks. The PegaPlus web server is openly available at pegaplus.zbh.uni-hamburg.de.
14 July 2026
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