Efficient drug lead discovery and optimization.

Area of Science:

• Computational chemistry and molecular modeling
• Drug discovery and medicinal chemistry
• Biomolecular simulations

Background:

• Early 1980s computational advances promised utility in molecular design for drug discovery.
• High-throughput screening and combinatorial chemistry emerged alongside computational methods like de novo design and virtual screening.
• Realizing these expectations required significant time and effort beyond initial technological advancements.

Purpose of the Study:

• To demonstrate the successful application of computer-aided methods for drug lead generation and optimization.
• To illustrate the use of de novo design and docking for identifying initial drug leads.
• To showcase free energy perturbation and Monte Carlo simulations for optimizing protein-inhibitor complexes.

Main Methods:

• De novo design incorporating molecular growing and docking techniques for lead generation.
• Free energy perturbation calculations combined with Monte Carlo statistical mechanics simulations for lead optimization.
• Application of a standardized protocol involving substituent modification and heterocycle exchange.

Main Results:

• Successful discovery of non-nucleoside inhibitors for HIV reverse transcriptase (HIV-RT).
• Identification of inhibitors targeting the binding of macrophage migration inhibitory factor (MIF) to its receptor CD74.
• Rapid advancement of initial low-micromolar activity leads to potent low-nanomolar inhibitors.

Conclusions:

• Computer-aided drug design has achieved significant success in generating and optimizing drug leads.
• The presented methodologies effectively accelerate the discovery of novel therapeutic agents.
• These computational approaches are crucial for advancing drug discovery pipelines.