How to drug a novel target in 500 molecules
Research conducted by the Variational AI team: Marshall Drew-Brook, Peter Guzzo, Ahmad Issa, Mehran Khodabandeh, Sara Omar, Jason Rolfe, and Ali Saberali. Searching through the space of synthesizable molecules to find an effective drug candidate is one of the most time-consuming and expensive steps of drug discovery. Once a protein mediating disease has been identified and some […]
Variational AI Selected by ImmVue Therapeutics to Power Immuno-Oncology Drug Discovery
Immuno-Oncology pioneer ImmVue Therapeutics to adopt Enki™Lead Generator to discover first-in-class cancer drugs.
Variational AI and Rakovina announce Collaboration
Rakovina announces potential multi-target engagement with Variational AI focused on DNA Damage Repair (DDR)
Why is QSAR so far behind other forms of machine learning, and what can be done to close the gap?
QSAR models struggle with extrapolation compared to conventional ML tasks like image recognition. Machine learning generalizes effectively when structured to align with its problem domain, suggesting that improving QSAR models may close this gap in drug discovery.
100 AI-generated molecules are worth a 1,000,000 molecule high-throughput screen
Generative AI in drug discovery is showing promise by optimizing molecule searches beyond traditional methods. Variational AI’s Enki algorithm created 100 AI-generated molecules that outperform 1,000,000 in conventional high-throughput screening, highlighting AI’s efficiency in exploring chemical space.
