AI Startups for ADMET Prediction
AI Startups for ADMET Prediction
AI-Guided Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Prediction
AI Startups to improve ADMET during drug screening
“However, safety of the target is accounted for at two different stages.”
“First, PandaOmics uses relevant existing information to find the safest and most promising therapeutic targets, while Chemsitry42 enables multi-parametric optimization of the molecules.”
“Chemistry42 also has around 500 medicinal chemistry filters that filter out compounds with structural motifs known to be toxic.”
said Alex Aliper, president of Insilico Medicine (BioSpace)
➡️ AI-Guided Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Prediction Startups 🚀
ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug’s pharmacokinetics and pharmacodynamics properties that can impact its efficacy and safety, that are considered some of the major causes of clinical attrition in the development of new chemical entities. So far, various ML or QSAR methods have been successfully integrated in the modeling of ADMET (AI-Guided Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Prediction) and this newsletter is an overview of AI Startups dedicated to ADMET Prediction:
🔦 DevsHealth is a Spanish DeepTech company using AI, Real-World Data (RWD) and molecular modeling to improve new anti-infectious treatments development. Their AI-based technology helps to optimize a rational design of new drugs, anticipate potential side effects and its behaviour in our body by predict ADME properties. By integrating, standardizing and curating several public-source databases at DevsHealth they are able to manage around 2.5M gene expression experiments, almost a million bioactive compounds and their biological activities and thousands of proteins and structures.
In 2023, the Bengaluru-based Foundation for Neglected Disease Research (FNDR) and the Spanish DevsHealth announced a collaboration to develop broad-spectrum antiviral agents for infections caused by flaviviruses such as dengue, Zika, West Nile virus, and Japanese encephalitis, among others. In this project, DevsHealth will be in charge of in silico studies and chemistry efforts and the FNDR will manage all in vitro and in vivo experiments.
🔦 The Chinese startup CarbonSilicon offers a drug discovery workflow leveraging AI-generated content (AIGC), self-supervised pre-training, reinforcement learning and physics-based modeling. The startup’s activity prediction solution, Inno-Docking, provides complete protein preparation, ligand preparation and intelligent setting of docking parameters. Additionally, Inno-Rescoring features AI-scoring functions to evaluate protein-ligand binding affinity. CarbonSilicon’s comprehensive druggability assessment involves three computational modules:
Inno-ADMET for ADMET properties,
ChemFH to filter frequent hit compounds and
Inno-SA to predict substructure-related toxicity.