Trends and Prospects in Computer-Aided Drug Design
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 3696
Special Issue Editors
Interests: biochemistry; computer-aided drug design; cheminformatics; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Interests: medicinal chemistry; computational chemistry; 3D-QSAR; machine learning; drug design; extraction of natural compounds; essential oil
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computer-aided drug design (CADD) comprises ligand-based (LB) and structure-based (SB) in silico methods, either conventional or modern (viz. artificial intelligence), being routinely applied in drug discovery in a continuously growing fashion. Upon reaching a good level of accuracy and predictability, CADD methods are nowadays trusted in prioritizing new and untested molecular entities to be acquired or synthesized, thus overcoming the intrinsic limitations of traditional synthetic protocols (low efficiency and high cost), allowing the reduction in unnecessary waste of in vitro and in vivo resources.
Therefore, either LB or SB CADD approaches are valuable tools to study how two different molecular entities can recognize each other, and to foresee their biological effect. Although many LB or SB CADD tools have been disclosed, new LB or SB CADD programs and applications are constantly being announced. Due to the limitations of current technology (either software or hardware), we are still far from reaching the full potential of LB or SB CADD application in drug design; the next-generation accuracy and predictability still have to be reached by means of improving the conventional LB or SB CADD approaches with the aid of artificial intelligence. Therefore, there is still urgency to continue the improvement of actual LB or SB procedures, and this represents the main goal of the herein announced Special Issue. Manuscripts are welcome to be submitted if reporting new approaches in LB or SB CADD or the application of the latest technology to either prioritize synthetically accessible molecules or optimize plant mixtures for further pharmacological evaluation.
The Special Issue “Trends and Prospects in Computer-Aided Drug Design” will embrace several LB or SB methods in CADD, such as:
- Ligand-based drug design: current methods vs. artificial intelligence;
- Ligand-based QSAR and QSPR modeling in drug design;
- Proteochemometrics modeling in drug design;
- Development of methods for quality assessments of topological descriptors;
- Artificial intelligence in the development of molecular descriptors;
- Ligand-based alignment software development;
- Ligand-based 3D QSAR in drug design;
- Ligand-based 3D QSAR software development;
- Ligand-based 3D pharmacophore modeling in drug design;
- Ligand-based 3D pharmacophore software development;
- Ligand-based design using medicinal chemistry plants as a source of bioactive compounds conducted by means of artificial intelligence;
- Analysis of drug mixture effects;
- Structure-based drug design: current methods vs. artificial intelligence;
- Predicting drug activity/properties;
- Structure-based 3D QSAR in drug design;
- 3D QSAR software development;
- Structure-based 3D pharmacophore modeling in drug design;
- Structure-based analysis of bioactive conformations;
- Small-molecule reversible docking application;
- Small-molecule covalent docking application;
- Protein–protein docking;
- Molecular docking software development;
- Molecular docking software comparison;
- Scoring function development;
- Scoring function comparison;
- SB/LB consensus scoring strategies in drug design;
- Virtual screening;
- Host–guest studies;
- Molecular dynamics as molecular docking tools;
- Design of selective drugs;
- Polypharmacology drug design;
- Drug toxicity prediction;
- De novo drug design using artificial intelligence;
- Drug bioavailability;
- Design of drug mixture or combinations;
- Automated workflows in drug design;
- Teaching methods in drug design;
- Reviews in CADD protocols.
Dr. Milan Mladenović
Dr. Rino Ragno
Guest Editors
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Keywords
- ligand-based (LB) drug design
- structure-based (SB) drug design
- QSAR
- 3D QSAR
- 3D pharmacophores
- artificial intelligence in drug design
- software development for drug design
- teaching methods in drug design
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