Department of Mathematics, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.
Department of Applied Mathematics, Faculty of Science, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.
Molecules. 2022 Jun 29;27(13):4169. doi: 10.3390/molecules27134169.
The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.
传统的药物发现方法既昂贵又耗时，但借助数学建模和计算药物设计方法，已经克服了其局限性。在此之前，寻找一种针对疾病的小分子候选药物非常昂贵，需要很长时间来筛选针对特定靶标的化合物。针对不同疾病（包括新发和再发疾病）开发新型靶标和小分子候选药物仍然是一个主要关注点，需要尽早开发新型治疗靶标和候选药物。在这方面，药物开发的计算和数学建模方法具有快速预测能力和成本效益的特点，具有优势。计算机辅助药物设计 (CADD) 技术利用不同的计算机程序和数学公式来理解靶标和药物的相互作用。确定小分子候选药物作为药物的传统方法有几个局限性，但 CADD 利用需要很少时间的新方法，可以以最小的成本准确预测针对特定疾病的化合物。因此，本综述旨在简要介绍识别新型靶标和治疗特定疾病的小分子候选药物的数学建模和计算方法。综述主要集中在生物靶标预测、基于结构和基于配体的药物设计方法、分子对接、虚拟筛选、药效团建模、定量构效关系 (QSAR) 模型、分子动力学模拟以及 MM-GBSA/MM-PBSA 方法，以及识别针对疾病的新型靶标和治疗方法的有价值的数据库资源和工具。这篇综述将帮助研究人员，为未来尽早开发针对疾病的有效药物和预防措施开辟道路。
