Conspectus The field of chemical research boasts a long history of developing software to
automate synthesis planning and reaction prediction. Early software relied heavily on expert …

Chemical reactions serve as foundational building blocks for organic chemistry and drug
design. In the era of large AI models, data-driven approaches have emerged to innovate the …

Deep learning (DL) methods have gained notable prominence in predictive and generative
tasks in molecular space. However, their application in chemical reactions remains grossly …

Drug development is a complex and time-consuming endeavor that traditionally relies on the
experience of drug developers and trial-and-error experimentation. The advent of artificial …

Designing synthetically accessible molecules and recommending analogs to
unsynthesizable molecules are important problems for accelerating molecular discovery. We …

The NLRP3 inflammasome plays a central role in the pathogenesis of various intractable
human diseases, making it an urgent target for therapeutic intervention. Here, we report the …

Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal
chemistry. However, traditional retrosynthesis predictions performed by chemists and rule …

Efficient chemical synthesis is critical for the production of organic chemicals, particularly in
the pharmaceutical industry. Leveraging machine learning to predict chemical synthesis and …

Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of
mRNA for vaccines and therapies by protecting it from rapid degradation. Among the …

Deep learning has revolutionized chemical research by accelerating the discovery of new
substances and enhancing the understanding of complex chemical systems. However …