How to drug a novel target in 500 molecules
Read more: How to drug a novel target in 500 moleculesResearch 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… Read More about How to drug a novel target in 500 molecules
Why is QSAR so far behind other forms of machine learning, and what can be done to close the gap?
Read more: 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… Read More about Why is QSAR so far behind other forms of machine learning, and what can be done to close the gap?
100 AI-generated molecules are worth a 1,000,000 molecule high-throughput screen
Read more: 100 AI-generated molecules are worth a 1,000,000 molecule high-throughput screenGenerative 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,… Read More about 100 AI-generated molecules are worth a 1,000,000 molecule high-throughput screen
Applicability domains are common in QSAR but irrelevant for conventional ML tasks
Read more: Applicability domains are common in QSAR but irrelevant for conventional ML tasksTraditional QSAR models are limited to interpolation within known chemical spaces, restricting drug discovery. In contrast, modern machine learning excels at extrapolation, opening new possibilities for exploring untapped chemical compounds… Read More about Applicability domains are common in QSAR but irrelevant for conventional ML tasks
