A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Cancers are the leading cause of death, causing around 10 million deaths annually by 2020. The most common cancers are those affecting the breast, lungs, colon, and rectum. However, it has been noted that cancer metastasis is more lethal than just cancer incidence and accounts for more than 90% of cancer deaths. Thus, early detection and prevention of cancer metastasis have the capability to save millions of lives. Finding novel biomarkers and targets for screening, determination of prognosis, targeted therapies, etc., are ways of doing so. In this review, we propose various sialyltransferases and neuraminidases as potential therapeutic targets for the treatment of the most common cancers, along with a few rare ones, on the basis of existing experimental and in silico data. This compilation of available cancer studies aiming at sialyltransferases and neuraminidases will serve as a guide for scientists and researchers working on possible targets for various cancers and will also provide data about the existing drugs which inhibit the action of these enzymes.