Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

We propose a novel deep learning approach for predicting drug–target interaction using a
graph neural network. We introduce a distance-aware graph attention algorithm to …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Highlights•Machine learning techniques (MLTs) are progressing the drug discovery
process.•Conventional MLTs require large data, lack transparency and are not …

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be
used to predict toxicity of various chemical compounds or classify the compounds based on …

Advances in deep neural network (DNN)-based molecular property prediction have recently
led to the development of models of remarkable accuracy and generalization ability, with …

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …

The band gap is an important parameter that determines light-harvesting capability of
perovskite materials. It governs the performance of various optoelectronic devices such as …