Drug-induced liver injury (DILI) is a major concern in the pharmaceutical industry, accounting for a significant number of drug failures and withdrawals. Traditional ways to predict DILI rely a lot on animal studies and in vitro tests. These can take a long time, cost a lot, and often don't accurately predict human DILI. As a result, there is a growing interest in using computational approaches to predict DILI. However, these methods fail to adequately consider detailed aspects of interspecies differences in drug pharmacological behavior, gene dysregulation due to the drugs, accurate drug chemistry, and integration of specific liver toxicity pathways. VeriSIM Life performed research aimed to solve these problems by leveraging knowledge-AI hybrid technology (hybrid AI) to provide robust solutions even in severely data-limited scenarios.
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
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