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 …

Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial
intelligence (AI) is increasingly employed to expedite the process. However, existing …

Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically
breaking down molecules into readily available building block compounds. Having a long …

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 …

Computer-assisted synthesis planning has become increasingly important in drug discovery.
While deep-learning models have shown remarkable progress in achieving high accuracies …

Motif extraction is an important task in motif based molecular representation learning.
Previously, machine learning approaches employing either rule-based or string-based …

Computer-assisted methods have emerged as valuable tools for retrosynthesis analysis.
However, quantifying the plausibility of generated retrosynthesis routes remains a …

Inferring appropriate synthesis reaction (ie, retrosynthesis) routes for newly designed
molecules is vital. Recently, computational methods have produced promising single-step …

The powerful data processing and pattern recognition capabilities of machine learning (ML)
technology have provided technical support for the innovation in computational chemistry …

In this work, we explore how existing datasets of quantum chemical properties can be
repurposed to build data-efficient downstream machine learning models, with a particular …