Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

In the past, conventional drug discovery strategies have been successfully employed to
develop new drugs, but the process from lead identification to clinical trials takes more than …

The data-science revolution is poised to transform the way photonic systems are simulated
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …

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 …

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

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

Over the past decade, the amount of biomedical data available has grown at unprecedented
rates. Increased automation technology and larger data volumes have encouraged the use …

Molecular docking is an established in silico structure-based method widely used in drug
discovery. Docking enables the identification of novel compounds of therapeutic interest …

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 …

Highlights•ADMET modeling plays a pivotal part in drug discovery.•Chemoinformatics has
evolved into robust machine learning approaches.•Comprehensive web-based platforms for …