Insilico Medicine’s End-to-End AI Platform Is Transforming Drug Discovery

Finding Novel – and Dual-Purpose –Targets using AI

Insilico used its end-to-end AI platform to discover a novel pan-fibrotic target and design a corresponding drug candidate for IPF in just 18 months. That lead drug candidate has since been through extensive target validation and is currently in Phase 1 trials in New Zealand and China.

Insilico Medicine, a clinical-stage endto-end artificial intelligence (AI)-driven drug discovery company headquartered in Hong Kong, is bringing innovation to drug discovery through its end-to-end AIpowered platform, Pharma.AI.

Since 2014, this platform has enabled the Company to discover novel targets and design new therapeutics at much greater speed and for significantly less cost than traditional drug discovery.

There are now over 30 therapeutics programmes in Insilico’s internal pipeline for indications including cancer, fibrosis, central nervous system diseases, and COVID-19. Its lead drug for idiopathic pulmonary fibrosis (IPF) is currently in Phase 1 trials, the first AI-discovered and AI-designed drug to reach this milestone.

The Launch of PandaOmics

Insilico’s end-to-end AI platform first launched with PandaOmics, a novel target discovery engine that relies on trillions of data points, including 5 million omics data samples – transcriptomics, genomics, epigenomics, proteomics, and single-cell data generated by the scientific community including 1.3 million compounds and biologics; 3.8 million patents; over $1t in grants; 342,000 clinical trials; and over 30 million publications.

The system then evaluates the targets using omics and text evidence.

PandaOmics facilitates systems biology research, focusing on the fundamentals of complex interactions within biological systems to identify disease signatures and actionable targets related to a disease. The AI models dynamically assess disease targets on measures such as novelty, accessibility by small molecules and biologics, and safety and tissue expression in order to best evaluate their potential druggability.

The target discovery engine allows for significant customisation, providing filters that can prioritise targets based on novelty, prior validation, or other features. Using a “time machine” approach, PandaOmics trains the AI to recognise patterns based on empirical data comparing initial target discovery during a defined time point to the level of pharmaceutical industry interest in a subsequent time period until the list overlaps with recognised “hot” targets in the industry.

In just two months, Insilico also used PandaOmics to uncover multiple targets specific to both ageing and age-associated disease, opening the door to future dualpurpose therapeutics. In the study, the results of which were published in the March 2022 issue of the journal Aging, Insilico used PandaOmics to perform target identification for 14 age-associated diseases and 19 nonage-associated diseases across multiple disease areas to identify targets of ageassociated diseases. PandaOmics revealed 145 genes that were considered potential ageing-related targets and mapped these into corresponding ageing hallmarks, including 69 high-confidence targets with high druggability, 48 medium novel targets with high or medium druggability, and 28 highly novel targets with medium druggability.

And a consortium of researchers from Harvard, Johns Hopkins, Mayo Clinic, University of Chicago, Shanghai University and other institutions recently used PandaOmics to find new targets for the debilitating neuromuscular disease amyotrophic lateral sclerosis (ALS). The platform drew on extensive patient data collected by the group Answer ALS. The AI software found 17 high-confidence and 11 novel therapeutic targets. This study has generated and experimentally validated 8 unreported therapeutic targets in the ALS animal model. The results were published in June 2022 in the journal Frontiers in Aging Neuroscience.

PandaOmics and the rest of Insilico Medicine’send-to-endPharma.AIplatformare ushering in a new era of AI-led drug discovery and design – revealing new possibilities for treating diseases with high unmet needs and presenting new opportunities for partnerships with pharmaceutical companies in order to accelerate their own therapeutic programmes.