Department of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States.
Department of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States.
J Chem Inf Model. 2024 May 13;64(9):3826-3840. doi: 10.1021/acs.jcim.4c00234. Epub 2024 May 2.
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.
近年来，计算方法的进步为药物发现带来了巨大的加速潜力。虽然数学建模和机器学习在预测药物-靶标相互作用和性质方面变得至关重要，但由于化学空间的广阔和复杂，计算药物发现仍有很大的潜力尚未被挖掘。本文基于我们最近发表的基于片段的药物发现（FBDD）计算方法，称为从筛选配体药物发现（FDSL-DD）中获得的片段数据库。FDSL-DD 使用计算机筛选从庞大的库中识别配体，在对它们进行片段化的同时，根据预测的结合亲和力和与目标亚结构域的相互作用，附加特定的属性。在本文中，我们进一步提出了一种两阶段优化方法，该方法利用预筛选信息来优化计算配体合成。我们假设使用预筛选信息进行优化可以缩小搜索空间并关注有前途的区域，从而提高候选配体的优化效果。第一阶段使用遗传算法将这些片段组装成更大的化合物，然后进行第二阶段的迭代优化，以产生具有增强生物活性的化合物。为了展示广泛的适用性，该方法学在三个不同的蛋白质靶标（人类实体瘤、细菌抗菌耐药性和 SARS-CoV-2 病毒）上进行了演示。综合使用 FDSL-DD 和两阶段优化方法比其他最先进的计算 FBDD 方法更有效地产生高亲和力的配体候选物。我们进一步表明，一种考虑药物相似性的多目标优化方法仍然可以产生具有高结合亲和力的潜在候选物。总的来说，这些结果表明，将详细的化学信息与受限的搜索框架相结合可以显著优化初始药物发现过程，为开发新的治疗方法提供了更精确和高效的途径。
