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

Area of Science:

• Computational Chemistry
• Drug Discovery
• Bioinformatics

Background:

• Computational methods, including machine learning, are crucial for drug discovery but face challenges with vast chemical spaces.
• Existing fragment-based drug discovery (FBDD) methods can be further optimized for efficiency.

Purpose of the Study:

• To introduce a two-stage optimization method for computational ligand synthesis, building upon the fragment databases from screened ligand drug discovery (FDSL-DD) approach.
• To improve the efficiency and precision of identifying high-affinity drug candidates by focusing on promising chemical regions.

Main Methods:

• Utilizing in silico screening to identify and fragment ligands, attaching attributes based on predicted binding affinity.
• Implementing a two-stage optimization: genetic algorithms for fragment assembly, followed by iterative refinement for enhanced bioactivity.
• Demonstrating the methodology on diverse targets: human solid cancers, bacterial antimicrobial resistance, and SARS-CoV-2.

Main Results:

• The proposed FDSL-DD with two-stage optimization yields high-affinity ligand candidates more efficiently than current state-of-the-art computational FBDD methods.
• A multiobjective optimization approach incorporating drug-likeness also produced potential high-affinity ligands.
• The integrated approach significantly optimizes the initial drug discovery process.

Conclusions:

• Integrating detailed chemical information with a constrained search framework markedly optimizes early-stage drug discovery.
• The developed methodology offers a more precise and efficient route for developing novel therapeutics.
• This approach accelerates the identification of drug candidates for critical diseases.