Freedom Space 3.0: ML-Assisted Selection of Synthetically Accessible Small Molecules.

Abstract

Advances in machine learning (ML) have revolutionized the exploration of chemical space, enabling the creation of subsets tailored for specific applications. Herein, we describe the development of Chemspace Freedom Space 3.0, a chemical library of synthetically accessible small molecules derived from ML-based filtering of building blocks. Our approach employs a model trained on a custom molecular representation to refine the selection of building blocks prior to enumeration, enhancing the quality and synthetic feasibility of the derived molecules. Freedom Space 3.0 comprises 5 billion molecules, generated using ten well-validated chemical transformations, and is complementary to Enamine REAL Space. We computationally evaluate the physicochemical properties, chemical diversity, and synthetic accessibility of the molecules from Freedom Space 3.0. Furthermore, experimental validation demonstrates a success rate of over 80% within a 4-6 week synthesis on a set of 700 molecules, proving the potential for Freedom Space 3.0 to accelerate hit finding and hit follow-up workflows.