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Journal article
Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening
Journal of chemical information and modeling, v 64(9), pp 3816-3840
02 May 2024
PMID: 38696451
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.
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Details
- Title
- Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening
- Creators
- Rohan Chandraghatgi - Drexel UniversityHai-Feng Ji - Department of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United StatesGail L Rosen - Drexel UniversityBahrad A Sokhansanj (Corresponding Author) - Drexel University
- Publication Details
- Journal of chemical information and modeling, v 64(9), pp 3816-3840
- Publisher
- ACS Publications
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Materials Science and Engineering; Chemistry
- Web of Science ID
- WOS:001226346500001
- Scopus ID
- 2-s2.0-85192223469
- Other Identifier
- 991021874515704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Chemistry, Medicinal
- Chemistry, Multidisciplinary
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications