Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of
drug development in the costly late stage, it has been widely recognized that drug ADMET …

Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …

We present SMILES-embeddings derived from the internal encoder state of a Transformer
[1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] …

Over the last decade, there has been significant progress in the field of machine learning for
de novo drug design, particularly in generative modeling of novel chemical structures …

This paper is an overview of the most significant and impactful interpretation approaches of
quantitative structure–activity relationship (QSAR) models, their development, and …

Highlights•Active learning is extensively used across various drug discovery stages.•Active
learning aids in solving multiple challenges in predicting compound–target …

Cross-validation is a common method to validate a QSAR model. In cross-validation, some
compounds are held out as a test set, while the remaining compounds form a training set. A …

There are numerous small molecular compounds around us to affect our health, such as
drugs, pesticides, food additives, industrial chemicals, and environmental pollutants. Over …

Predicting bioactivity and physical properties of small molecules is a central challenge in
drug discovery. Deep learning is becoming the method of choice but studies to date focus on …

In silico drug-target interaction (DTI) prediction is an important and challenging problem in
biomedical research with a huge potential benefit to the pharmaceutical industry and …