From Computational Design to Experimental Validation: Integrated Workflows in Drug Discovery

Dear Colleagues,

Computational methods are now an established component of drug discovery, supporting target characterization, hit identification, binding-mode analysis, lead optimization, and the prediction of molecular and pharmacological properties. Artificial intelligence and machine-learning approaches are further expanding the range of computational strategies available for drug discovery, from molecular property prediction and compound prioritization to de novo design, target identification, and the analysis of complex biological data. Their practical value, however, ultimately depends on a close connection with experiment.  Computational and artificial-intelligence-based predictions are most informative when they guide the selection or design of molecules and experiments, and when experimental findings are used to test, interpret, and refine the underlying models. The integration of computational design, AI-assisted drug discovery, and experimental validation is therefore essential for improving the reliability, interpretability, and practical relevance of modern drug discovery workflows.  This inter-journal Special Issue aims to present recent advances in which computational methods, including artificial intelligence applied to drug discovery, are integrated with experimental investigation within a coherent research workflow. We welcome contributions covering different stages of the discovery process, from target and hit identification to mechanistic studies, lead optimization, drug repurposing, and the evaluation of selectivity, pharmacokinetic properties, and safety.  Original research articles should establish a clear relationship between the computational hypothesis and the experimental work performed. Experimental studies may include compound synthesis and characterization, biochemical or biophysical assays, structural studies, target-engagement measurements, cellular models, omics-based analyses, pharmacological studies, or in vivo evaluation. Methodological articles, reviews, and perspectives addressing validation, benchmarking, interpretability, reproducibility, and best practices in computational and AI-enabled drug discovery are also encouraged.  Topics of interest include, but are not limited to, the following: 

• Structure- and ligand-based drug design;
• Artificial intelligence and machine learning for drug discovery;
• AI-assisted molecular property and bioactivity prediction;
• Generative AI and de novo molecular design for drug discovery;
• Virtual screening and computational hit identification;
• Molecular docking, molecular dynamics, and free-energy calculations;
• Quantum-mechanical and multiscale modeling;
• QSAR/QSPR and chemoinformatics;
• Computational target identification, binding-site analysis, and druggability assessment;
• AI-supported analysis of chemical, biological, structural, and omics data for drug discovery;
• Computationally guided synthesis, structure–activity relationship studies, and lead optimization;
• Experimental validation of predicted binding modes, molecular targets, and mechanisms of action;
• Prediction and experimental assessment of bioactivity, selectivity, ADME, and toxicity;
• Drug repurposing and polypharmacology;
• Prospective validation, benchmarking, interpretability, reproducibility, and iterative computational–experimental workflows.

We welcome original research articles, reviews, perspectives, and methodological contributions showing how the integration of computational approaches, artificial intelligence, and experimental validation can improve the robustness and translational value of drug discovery research.

You may choose our Joint Special Issue in Molecules.

Dr. Carmen Cerchia
Prof. Dr. Antonio Lavecchia
Guest Editors

Manuscript Submission Information

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• computer-aided drug design
• computational drug discovery
• machine learning
• artificial intelligence
• experimental validation
• molecular modeling
• structure-based drug design
• ligand-based drug design
• virtual screening
• molecular docking
• molecular dynamics
• chemoinformatics
• QSAR
• molecular property prediction
• de novo molecular design
• medicinal chemistry
• hit identification
• lead optimization
• drug repurposing
• structure–activity relationships