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.

中文翻译：

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通过基于配体的虚拟预筛选的进化优化简化基于计算片段的药物发现

计算方法的最新进展有望显着加速药物发现。虽然数学建模和机器学习在预测药物与靶点相互作用和特性方面变得至关重要，但由于化学空间广阔而复杂，计算药物发现仍有尚未开发的潜力。本文基于我们最近发布的基于计算片段的药物发现 (FBDD) 方法，称为筛选配体药物发现片段数据库 (FDSL-DD)。 FDSL-DD 用于计算机筛选，从庞大的文库中识别配体，将其片段化，同时根据预测的结合亲和力和与目标子域的相互作用附加特定属性。在本文中，我们进一步提出了一种两阶段优化方法，利用预筛选的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专注于有希望的区域，从而改善候选配体的优化。第一个优化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细化，以产生具有增强生物活性的化合物。为了证明其广泛的适用性，该方法在人类实体癌、细菌抗菌素耐药性和 SARS-CoV-2 病毒中发现的三种不同蛋白质靶标上进行了论证。结合起来，所提出的 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候选配体。我们进一步表明，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配体。总体而言，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着优化初始药物发现过程，为开发新疗法提供更精确、更有效的途径。