Prospective de novo drug design with deep interactome learning

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: April 22, 2024

Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- structure-based generation of drug-like molecules. This method capitalizes on the unique strengths both graph neural networks language models, offering an alternative need application-specific reinforcement, transfer, or few-shot learning. It enables “zero-shot" construction compound libraries tailored bioactivity, synthesizability, structural novelty. In order proactively evaluate interactome framework protein design, potential new ligands targeting binding site human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs chemically synthesized computationally, biophysically, biochemically characterized. Potent PPAR partial agonists identified, demonstrating favorable activity desired selectivity profiles nuclear receptors off-target interactions. Crystal structure determination ligand-receptor complex confirms anticipated mode. successful outcome positively advocates de application in bioorganic medicinal chemistry, enabling creation innovative bioactive

Language: Английский

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

Expert Opinion on Drug Discovery, Journal Year: 2024, Volume and Issue: 19(6), P. 683 - 698

Published: May 10, 2024

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) target variables PK parameters), are being increasingly used this purpose. To embed ML models prediction into workflows guide future development, a solid understanding their applicability, advantages, limitations, synergies with other approaches necessary.

Language: Английский