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Recent Advances in Computer-Aided Drug Design and Drug Discovery, 2nd Edition

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 34

Special Issue Editors


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Guest Editor
NYU-ECU Center for Computational Chemistry, NYU Shanghai, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China
Interests: computational chemistry; MD simulation; computational drug design; protein–protein interaction; free energy calculation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Research Center for Material Genome Engineering, Sichuan University, Wangjiang Road No. 29, Chengdu, China
Interests: theoretical simulation of molecular interactions on supermolecule and protein systems; theoretical study of chemical/enzymatic reaction mechanisms; theoretical study of material systems via combined muti-scale approaches and machine learning algorithms; development of high-throughput computing platform for material systems

Special Issue Information

Dear Colleagues,

After the great success of this Special Issue's first edition, we are pleased to inform you that Molecules will launch the second edition of “Recent Advances in Computer-Aided Drug Design and Drug Discovery”.

Computer-Aided Drug Designing (CADD) has gained wide popularity among biologists and chemists as a part of an interdisciplinary drug discovery approach. It plays a vital role in the discovery, design, and analysis of drugs in the pharmaceutical industry. It is extensively used to reduce costs and time and speed up the early-stage development of new biologically active molecules. To date, several approved drugs have been developed with the aid of CADD. In this Special Issue, we will present manuscripts on the following types of CADD: Structure-Based Drug Designing (SBDD), including molecular docking and molecular dynamic simulations; Ligand-Based Drug Designing (LBDD), including quantitative structure–activity relationship (QSAR) modelling; Pharmacophore-based drug designing (PBDD); and Fragment-Based Drug Designing (FBDD), in addition to the theory, such as artificial intelligence (AI), behind the types of CADD and their applications.

Prof. Dr. John Zhang
Prof. Dr. Dingguo Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer-aided drug design
  • structure-based drug design
  • ligand-based drug design
  • fragment-based drug design
  • pharmacophore
  • QSAR
  • molecular modelling
  • artificial intelligence

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