Objective

• The core challenge in computational and medicinal chemistry is to identify molecules that effectively bind to specific biological targets and possess the necessary properties to become clinical candidates. This begins with pinpointing a druggable pocket on the target protein, followed by the design and synthesis of compounds that can interact with it. The process is iterative and involves optimizing the compound's properties for efficacy and safety, which is time-consuming and resource intensive.
• Despite advancements, the success rate remains low, with many candidates failing in later development stages. The key challenge is to enhance efficiency and success in translating potential molecules into viable therapies.

Methodology

The computational tools and medicinal chemistry principles apply to design and optimize a novel therapeutic agent. By integrating in silico methods with medicinal chemistry approaches, we demonstrate the synergy between computational predictions and experimental validation.

• The study begins with identifying a critical enzyme implicated in the disease pathway. Structural data from X-ray crystallography provides insights into the active site and key interactions.
• Using our proprietary AI/ML tool in compound design, we design compounds and/or analyze large datasets to predict interactions between molecules and targets. This approach enhances the identification of druggable sites and optimizes compound design.
• Machine learning accelerates high-throughput screening by quickly processing results to identify promising compounds.
• Top candidates from the docking study undergo MD simulations to evaluate their stability and behavior in a dynamic biological environment. This approach provides additional confidence in the selected compounds' potential efficacy.
• AI-driven generative models create novel compounds with desirable properties, while predictive toxicology identifies potential side effects early in the development process.
• The lead compounds identified through computational methods are chemically modified to improve their pharmacokinetic and pharmacodynamic properties. This process involves enhancing solubility, bioavailability, and metabolic stability while minimizing potential toxicity.
• Medicinal chemists analyse the structure-activity relationship (SAR) to identify functional groups that contribute to the compound's activity and refine the structure accordingly.
• The optimized compounds are synthesized and tested in vitro for their inhibitory activity against the target enzyme. Further in vivo studies are conducted to evaluate their therapeutic potential.

Outcomes / Impact

The integration of computational and medicinal chemistry led to the identification of a potent inhibitor with a favorable safety profile. The compound demonstrated significant efficacy in preclinical models, paving the way for clinical trials.