Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …

We propose and discuss an efficient scheme for the in silico sampling for parts of the
molecular chemical space by semiempirical tight-binding methods combined with a meta …

The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent
years. In this Review, we studied the growth and distribution of AI-related chemistry …

Difficulties in interpreting machine learning (ML) models and their predictions limit the
practical applicability of and confidence in ML in pharmaceutical research. There is a need …

Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …