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Pharmaceuticals
Special Issues
Chemoinformatics and Drug Design, 2nd Edition
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Chemoinformatics and Drug Design, 2nd Edition

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 1761

Special Issue Editor

Dr. Weifan Zheng

E-Mail Website
Guest Editor
Department of Pharmaceutical Sciences, BRITE Institute, NC Central University, Durham, NC 27707, USA
Interests: cheminformatics; AI/machine learning; drug discovery; drug repurposing

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) have revolutionized various scientific fields, with cheminformatics, drug discovery, and drug repurposing being at the forefront of these advancements. Compared with traditional computational methods, the integration of AI/ML technologies promises to significantly enhance the efficiency and accuracy of predicting drug–target–disease interactions and biological activities.

This Special Issue will explore the cutting-edge of artificial intelligence and machine learning technologies for cheminformatics, drug discovery, and drug repurposing. It aims to present AI/ML studies on developing innovative models and novel representation methods for chemical entities (e.g., chemical structures and names), biological entities (e.g., target sequences, structures, and names), and clinical diseases. Our focus will be on predictive models that enhance understanding and prediction of drug–target–disease interactions and biological activity. Contributions will emphasize the efficient utilization of AI/ML technologies in cheminformatics and drug discovery research, as well as practical applications for identifying new therapeutic uses for existing drugs. This collection will demonstrate AI’s transformative potential in accelerating drug discovery processes and improving drug repurposing efforts. We also welcome research articles on novel methods for knowledge graph-based modeling, text mining, as well as applications of large language models (LLM) in cheminformatics research.

Dr. Weifan Zheng
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Pharmaceuticals is an international peer-reviewed open access monthly 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 2900 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

  • artificial intelligence (AI)
  • machine learning (ML)
  • representation learning
  • cheminformatics
  • drug discovery
  • drug repurposing
  • biological activity prediction
  • drug–target–disease interactions

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 7695 KiB  
Article
Rational Approach to New Chemical Entities with Antiproliferative Activity on Ab1 Tyrosine Kinase Encoded by the BCR-ABL Gene: An Hierarchical Biochemoinformatics Analysis
by Vitor H. da S. Sanches, Cleison C. Lobato, Luciane B. Silva, Igor V. F. dos Santos, Elcimar de S. Barros, Alexandre de A. Maciel, Elenilze F. B. Ferreira, Kauê S. da Costa, José M. Espejo-Román, Joaquín M. C. Rosa, Njogu M. Kimani and Cleydson B. R. Santos
Pharmaceuticals 2024, 17(11), 1491; https://doi.org/10.3390/ph17111491 - 6 Nov 2024
Viewed by 1260
Abstract
Background: This study began with a search in three databases, totaling six libraries (ChemBridge-DIVERSet, ChemBridge-DIVERSet-EXP, Zinc_Drug Database, Zinc_Natural_Stock, Zinc_FDA_BindingDB, Maybridge) with approximately 2.5 million compounds with the aim of selecting potential inhibitors with antiproliferative activity on the chimeric tyrosine kinase encoded by the [...] Read more.
Background: This study began with a search in three databases, totaling six libraries (ChemBridge-DIVERSet, ChemBridge-DIVERSet-EXP, Zinc_Drug Database, Zinc_Natural_Stock, Zinc_FDA_BindingDB, Maybridge) with approximately 2.5 million compounds with the aim of selecting potential inhibitors with antiproliferative activity on the chimeric tyrosine kinase encoded by the BCR-ABL gene. Methods: Through hierarchical biochemoinformatics, ADME/Tox analyses, biological activity prediction, molecular docking simulations, synthetic accessibility and theoretical synthetic routes of promising compounds and their lipophilicity and water solubility were realized. Results: Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the top100/base (600 structures), in comparison with the commercial drug imatinib, showed that only nine exhibited the desired properties. In the prediction of biological activity, the results of the nine selected structures ranged from 13.7% < Pa < 65.8%, showing them to be potential protein kinase inhibitors. In the molecular docking simulations, the promising molecules LMQC01 and LMQC04 showed significant values in molecular targeting (PDB 1IEP—resolution 2.10 Å). LMQC04 presented better binding affinity (∆G = −12.2 kcal mol−1 with a variation of ±3.6 kcal mol−1) in relation to LMQC01. The LMQC01 and LMQC04 molecules were advanced for molecular dynamics (MD) simulation followed by Molecular Mechanics with generalized Born and Surface Area solvation (MM-GBSA); the comparable, low and stable RMSD and ΔE values for the protein and ligand in each complex suggest that the selected compounds form a stable complex with the Abl kinase domain. This stability is a positive indicator that LMQC01 and LMQC04 can potentially inhibit enzyme function. Synthetic accessibility (SA) analysis performed on the AMBIT and SwissADME webservers showed that LMQC01 and LMQC04 can be considered easy to synthesize. Our in silico results show that these molecules could be potent protein kinase inhibitors with potential antiproliferative activity on tyrosine kinase encoded by the BCR-ABL gene. Conclusions: In conclusion, the results suggest that these ligands, particularly LMQC04, may bind strongly to the studied target and may have appropriate ADME/Tox properties in experimental studies. Considering future in vitro or in vivo assays, we elaborated the theoretical synthetic routes of the promising compounds identified in the present study. Based on our in silico findings, the selected ligands show promise for future studies in developing chronic myeloid leukemia treatments. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design, 2nd Edition)
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