The Machine Learning, Applications in the Discovery of New Bioactive Molecules, 2nd Edition
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".
Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1728
Special Issue Editor
Interests: computational medicinal chemistry; design of new drugs; anti-infectious agents; anti-cancer agents; in silico methods; virtual screening; molecular docking; de novo design; homology modelling; pharmacophore modelling; molecular dynamics; Monte Carlo; quantum chemistry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Various computational approaches support the development of new biologically active substances at all stages. Among them, machine learning (ML) methods are gaining great popularity due to their high prediction power and ability to handle a large amount of data in a relatively short time. ML-based tools not only assist in the search for new ligands with a particular activity profile but also help to predict and optimize physicochemical and pharmacokinetic properties, while avoiding side effects. In addition, ML also takes part in the enumeration of compound libraries, covering desired activity and property profiles via the application of deep learning methods.
The present Special Issue aims to cover all aspects of ML-based tool applications in computer-aided drug design—from ligand-based approaches (in both activity and physicochemical/ADMET property predictions) in structure-based protocols (e.g., for post-processing of docking results) to the generation of new ligands (e.g., with the use of deep learning). Manuscripts presenting methods that are experimentally verified are of particular interest.
Dr. Rita Guedes
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- computer-aided drug design
- ligand-based approaches
- structure-based approaches
- in silico compound profiling
- virtual screening
- ADMET property evaluation
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