News Release

New paper explores Insilico Medicine’s generative AI drug design platform Chemistry42

Peer-Reviewed Publication

InSilico Medicine

Scientists from clinical stage artificial intelligence (AI)-driven drug discovery company Insilico Medicine (“Insilico”) have published in the Journal of Chemical Information and Modeling on the use of Insilico’s generative AI Chemistry42 platform to quickly design novel molecular structures that target proteins that play essential roles in disease progression. 

Launched in 2020, the Chemistry42 platform connects state-of-the-art generative AI algorithms with medicinal and computational chemistry methodologies to generate novel molecular structures with optimized properties. The platform has been leveraged by over 20 pharmaceutical companies and over 15 external and 30 internal programs.

“Chemistry42 is an active learning system that relies on 42 generative algorithms that have been pre-trained to design drug-like molecular structures. They draw from a variety of molecule representations, base algorithms, and strategies to explore the chemical space thoroughly,” says Petrina Kamya, Ph.D., Head of AI Platforms at Insilico Medicine. “The system benefits from ongoing partnerships with pharma companies whose feedback has strengthened and validated its performance and results over time.” 

The generative models include generative autoencoders, generative adversarial networks, flow-based approaches, evolutionary algorithms, and language models, among others. 

A major advantage of the system is its customizable reward function. As the molecular structures are generated, they are dynamically assessed using the reward function and 3D physics-based modules. Each module scores the generated molecules and together with the generative algorithms, optimizes those molecules that are most likely to succeed in terms of potency, metabolic stability, synthetic accessibility and more. The novel molecules are further ranked based on their ADME and selectivity profiles. 

Insilico has published a number of seminal papers in the field of generative chemistry. This paper focuses on two specific case studies. The first, published in Nature Biotechnology in 2018, relied on the earliest incarnation of Chemistry42, known as the GENTRL model. Trained on the ZINC dataset and then further focused on DDR1 inhibitors and a publicly available kinase inhibitor dataset, the system produced 40 structures. Of these, six were selected for synthesis and experimental validation. After 35 days, the compounds were synthesized and tested in vitro to inhibit DDR1 activity, and four were found to be active. 

“As generative AI is making headlines generating fancy text and images, some of the generative chemistry tools that were first proposed by our group in 2015 with the first theoretical publications in 2016 and experimental papers in 2018 have now reached industrial strength and deep market penetration,” says Alex Zhavoronkov, Ph.D., founder and CEO of Insilico Medicine. “The many generative engines are now validated with over 40 generative algorithms integrated in Chemistry42. Also, we have clinical-stage therapeutic programs generated with the help of this platform. I am very happy to see the Application Paper, which outlines our path from theory into practice, that many scientists using Chemistry42 can now cite when publishing their AI-generated molecules.”

The second case study is from Insilico’s recent application of an AlphaFold2 predicted protein structure to the Chemistry42 platform to generate a novel inhibitor for CDK20, a promising drug target for hepatocellular carcinoma. In total, 8,918 molecules were designed, and 54 that had unique scaffolds with diverse hinge binder profiles were prioritized. A hit molecule was identified, and two compounds displayed strong potency for the intended target in a second round. The research was done in partnership with AI experts from the University of Toronto’s Acceleration Consortium and published in the journal Chemical Science. The findings demonstrated how AI systems can work together to produce novel therapeutics where structural data is limited. 

The Chemistry42 platform is designed for use by drug hunters and allows for ongoing collaboration to advance the rapidly evolving field of AI drug discovery. 


About Insilico Medicine

Insilico Medicine, a clinical-stage end-to-end artificial intelligence (AI)-driven drug discovery company, connects biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine delivers breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system (CNS), and aging-related diseases.

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