Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While math-ematical 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 advances a novel computational fragment-based drug discovery (FBDD) method called Fragment Databases from Screened Ligands Drug Discovery (FDSL-DD), which aims to streamline drug design by applying a two-stage optimization process. In this ap-proach, in silico screening identifies ligands from a vast library, which are then fragmentized while attaching specific at-tributes based on predicted binding affinity and interaction with the target sub-domain. This process both shrinks the search space and focuses on promising regions within it. The first optimization stage assembles these fragments into larger com-pounds using evolutionary strategies, and the second stage iteratively refines resulting compounds for enhanced bioac-tivity. The methodology is validated across three diverse protein targets involved in human solid cancers, bacterial antimi-crobial resistance, and SARS-CoV-2 viral entry, demonstrating the approachs broad applicability. Using the proposed FDSL-DD and two-stage optimization approach yields high-affinity ligand candidates more efficiently than other state-of-the-art computational methods. Furthermore, a multiobjective optimization method is presented that accounts for druglikeness while still producing potential candidate ligands with high binding affinity. Overall, the results demonstrate that integrat-ing 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.