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Empowering Radical Cross-Coupling Chemistry through AI-Driven Retrosynthesis and Real-World Validation.
Artificial Intelligence in Chemistry, Computer-Aided Synthesis Planning (CASP), Synthetic Methodology, Medicinal Chemistry, Digital Chemistry Tools Ganna Posternak Artificial Intelligence in Chemistry, Computer-Aided Synthesis Planning (CASP), Synthetic Methodology, Medicinal Chemistry, Digital Chemistry Tools Ganna Posternak

Empowering Radical Cross-Coupling Chemistry through AI-Driven Retrosynthesis and Real-World Validation.

This article explores how radical cross-coupling chemistry and AI-driven retrosynthesis are converging to reshape modern synthesis. By examining case studies from Baran-lab RCC methods and evaluating ChemAIRS® route-planning performance, we show how AI is increasingly capable of recognizing, ranking, and proposing radical disconnections with expert-level intuition. Combined with new hydrazide-based redox-free RCC protocols and commercially available sulfonyl hydrazides, this integration is transforming radical chemistry from a specialized technique into a practical, accessible tool for medicinal chemists.

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Human–AI Synergy in Retrosynthetic Analysis and Route Optimization of Balinatunfib
Medicinal Chemistry, Drug Development, Synthetic Chemistry, AI in Pharmaceuticals, Process Optimization Ganna Posternak Medicinal Chemistry, Drug Development, Synthetic Chemistry, AI in Pharmaceuticals, Process Optimization Ganna Posternak

Human–AI Synergy in Retrosynthetic Analysis and Route Optimization of Balinatunfib

Discover how AI-driven retrosynthesis and human expertise converge in the development of Balinatunfib (SAR-441566), a first-in-class small-molecule TNF-α inhibitor. Learn about its unique allosteric mechanism, clinical progress, and how ChemAIRS revolutionizes route optimization, enabling cost-effective and scalable synthesis in modern drug discovery.

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