AILDE

AILDE performs in silico ligand directing evolution to optimize hit compounds using the Computational Substitution Optimization (CSO) protocol by automated computational substitution, energy minimization, and binding affinity evaluation for drug lead discovery.

Key Features:

• Computational Substitution Optimization (CSO) protocol: Implements the CSO protocol that integrates automated computational substitution, energy minimization, and binding affinity evaluation.
• Automated Computational Substitution: Performs systematic minor chemical modifications on a hit compound scaffold to generate and evaluate analogs.
• Energy Minimization and Binding Affinity Evaluation: Applies energy minimization to refine ligand conformations and evaluates predicted binding affinities to prioritize candidates against target proteins.
• Ligand efficiency and selectivity focus: Emphasizes minor modifications to preserve or enhance ligand efficiency and identify potent, selective leads.
• Exploration of accessible chemical space: Explores accessible chemical space to reduce experimental synthesis and accelerate lead identification.

Scientific Applications:

• Hit-to-lead optimization: Supports hit-to-lead workflows by rapidly generating and prioritizing analogs for experimental follow-up.
• Kinase inhibitor design (c-Met): Applied to mesenchymal-epithelial transition factor (c-Met) kinase inhibitors, where synthesis of eight compounds yielded compound 5g with an approximately 1,000-fold improvement in in vitro activity and excellent in vivo antitumor efficacy.

Methodology:

Implements the CSO protocol starting from initial scaffold identification of a known active hit, followed by in silico generation of analogs via minor chemical substitutions, energy minimization for predicted stability, and binding affinity evaluation to rank candidates.