"Several birds with one stone": exploring the potential of AI methods for multi-target drug design.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Muhetaer Mukaidaisi, Madiha Ahmed, Karl Grantham, Aws Al-Jumaily, Shoukat Dedhar, Michael Organ, Alain Tchagang, Jinqiang Hou, Syed Ejaz Ahmed, Renata Dividino, Yifeng Li
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引用次数: 0

Abstract

Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies have been adopted to cut costs and speed up the drug development process, serving as promising in silico approaches to efficiently design novel drug candidates targeting various health conditions. Most existing AI-driven drug discovery studies follow a single-target approach which focuses on identifying compounds that bind a target (i.e., one-drug-one-target approach). Polypharmacology is a relatively new concept that takes a systematic approach to search for a compound (or a combination of compounds) that can bind two or more carefully selected protein biomarkers simultaneously to synergistically treat the disease. Recent studies have demonstrated that multi-target drugs offer superior therapeutic potentials compared to single-target drugs. However, it is intuitively thought that searching for multi-target drugs is more challenging than finding single-target drugs. At present, it is unclear how AI approaches perform in designing multi-target drugs. In this paper, we comprehensively investigated the performance of multi-objective AI approaches for multi-target drug design. Our findings are quite counter-intuitive demonstrating that, in fact, AI approaches for multi-target drug design are able to efficiently generate more high-quality novel compounds than the single-target approaches while satisfying a number of constraints.

"一石数鸟":探索人工智能方法在多靶点药物设计方面的潜力。
药物发现是一个耗时且昂贵的过程。人工智能(AI)方法已被采用来降低成本并加快药物开发过程,作为一种有前途的硅学方法,可有效设计出针对各种健康状况的新型候选药物。现有的大多数人工智能驱动的药物发现研究采用的是单靶点方法,重点是识别与靶点结合的化合物(即一药一靶点方法)。多药理学是一个相对较新的概念,它采用系统的方法来寻找一种化合物(或化合物组合),这种化合物可以同时结合两种或两种以上精心挑选的蛋白质生物标志物,从而协同治疗疾病。最近的研究表明,与单靶点药物相比,多靶点药物具有更优越的治疗潜力。然而,人们直观地认为,寻找多靶点药物比寻找单靶点药物更具挑战性。目前,人工智能方法在设计多靶点药物方面的表现尚不明确。本文全面研究了多目标人工智能方法在多靶点药物设计中的表现。我们的研究结果非常反直觉地表明,事实上,多靶点药物设计人工智能方法能够比单靶点方法有效地生成更多高质量的新型化合物,同时还能满足一系列约束条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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