Highlights•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint
and similarity analysis.•Machine learning models for virtual screening.•Future challenges …

This review is focused on several machine learning approaches used in chemoinformatics.
Machine learning approaches provide tools and algorithms to improve drug discovery. Many …

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths
annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer …

Here we show that Generative Topographic Mapping (GTM) can be used to explore the
latent space of the SMILES-based autoencoders and generate focused molecular libraries …

Chemists have to a large extent gained their knowledge by doing experiments and thus
gather data. By putting various data together and then analyzing them, chemists have …

Because of their unique effects and relatively low side effects, herbal medicine has been
gaining popularity all over the world. Quality control is a challenge to ensure safety, efficacy …

Here, the utility of Generative Topographic Maps (GTM) for data visualization, structure‐
activity modeling and database comparison is evaluated, on hand of subsets of the …

Abstract The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular
structures opened a novel paradigm in compound design, weaknesses (stability & feasibility …

Analysis of marketed drugs and commercial vendor libraries used in high-throughput
screening suggests that the medicinally relevant chemical space may be expanded to …

The purpose of this research was to derive promising technologies for the transport of
hydrogen fuel cells, thereby supporting the development of research and development …