Inductive Bio Announces Successful Outcomes from Drug Optimization Collaboration with Nested Therapeutics
Tuesday, July 30, 2024
Inductive Bio, a pioneer in utilizing machine learning (ML) to advance small molecule drug discovery, has shared new insights from their recent collaboration with Nested Therapeutics. Nested, recognized for its cutting-edge precision medicine platform addressing difficult cancers, partnered with Inductive to integrate ML models for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) and computational potency predictions. These findings, published in ACS Medicinal Chemistry Letters, underscore how this integration improved the selection of synthetic targets and enhanced overall design quality.
The process of small molecule optimization is both expensive and time-consuming, requiring a balance between potency and ADMET properties. By combining Inductive’s sophisticated ADMET models, which use deep learning techniques and proprietary datasets, with Nested’s platform for predicting potency in challenging pockets, the partnership effectively reduced the number of compounds that needed synthesis and expedited lead optimization.
Yongxin Han, EVP and Head of Drug Discovery at Nested Therapeutics, noted, “The integration of Inductive Bio’s ADMET models into our predictive tools allowed us to prioritize designs with superior drug-like properties. This collaboration enabled quicker iteration and optimization of lead compounds while overcoming significant ADMET challenges.”
Josh Haimson, co-founder and CEO of Inductive Bio, said, “We are excited about the success of our collaboration with Nested Therapeutics. This publication highlights the significant role of ML models for ADMET in accelerating drug optimization and offers a valuable framework for others to implement. We are eager to continue supporting our partners in reaching their drug discovery goals.”
Source: inductive.bio
