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 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.

Fragment databases from screened ligands for drug discovery (FDSL-DD).

Fragment-based drug design (FBDD) is one major drug discovery method employed in computer-aided drug discovery. Due to its inherent limitations, this process experiences long processing times and limited success rates. Here we present a new Fragment Databases from Screened Ligands Drug Design method (FDSL-DD) that intelligently incorporates information about fragment characteristics into a fragment-based design approach to the drug development process. The initial step of the FDSL-DD is the creation of a fragment database from a library of docked, drug-like ligands for a specific target, which deviates from the traditional in silico FBDD strategy, incorporating structure-based design screening techniques to combine the advantages of both approaches. Three different protein targets have been tested in this study to demonstrate the potential of the created fragment library and FDSL-DD. Utilizing the FDSL-DD led to an increase in binding affinity for each protein target. The most substantial increase was exhibited by the ligand designed for TIPE2, with a 3.6 kcalmol-1 difference between the top ligand from the FDSL-DD and top ligand from the high throughput virtual screening (HTVS). Using drug-like ligands in the initial HTVS allows for a greater search of chemical space, with higher efficiency in fragments selection, less grid boxes, and potentially identifying more interactions.