Journal
ACS OMEGA
Volume 7, Issue 22, Pages 18699-18713Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.2c01404
Keywords
-
Categories
Funding
- NIH from NIGMS [R44GM122196-02A1, 2R44GM122196-04A1]
- NCCAM [3R43AT010585-01S1]
- NIEHS [1R43ES031038-01]
- National Institute of Environmental Health Sciences of the National Institutes of Health [R43ES031038]
Ask authors/readers for more resources
Generative machine learning models are widely used in drug discovery to generate new molecules and explore molecular space. However, these models face challenges when combined with scoring functions and the generated compounds may not be synthesizable. In this article, the authors introduce a suite of automated tools called MegaSyn, which combines multiple generative models and molecular analysis methods to generate synthesizable compounds with drug potential.
Generative machine learning models have become widely adopted in drug discovery and other fields to produce new molecules and explore molecular space, with the goal of discovering novel compounds with optimized properties. These generative models are frequently combined with transfer learning or scoring of the physicochemical properties to steer generative design, yet often, they are not capable of addressing a wide variety of potential problems, as well as converge into similar molecular space when combined with a scoring function for the desired properties. In addition, these generated compounds may not be synthetically feasible, reducing their capabilities and limiting their usefulness in real-world scenarios. Here, we introduce a suite of automated tools called MegaSyn representing three components: a new hill-climb algorithm, which makes use of SMILES-based recurrent neural network (RNN) generative models, analog generation software, and retrosynthetic analysis coupled with fragment analysis to score molecules for their synthetic feasibility. We show that by deconstructing the targeted molecules and focusing on substructures, combined with an ensemble of generative models, MegaSyn generally performs well for the specific tasks of generating new scaffolds as well as targeted analogs, which are likely synthesizable and druglike. We now describe the development, benchmarking, and testing of this suite of tools and propose how they might be used to optimize molecules or prioritize promising lead compounds using these RNN examples provided by multiple test case examples.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available