Covalent small-molecule ligands have re-emerged as powerful tools in drug discovery, offering prolonged target engagement, enhanced potency, and the ability to modulate proteins once considered undruggable. However, the rational design and virtual screening (VS) of covalent ligands remain challenging. Many docking tools cannot accurately model the energetics of covalent bond formation, often requiring more rigorous quantum mechanical (QM) or semi-empirical QM calculations for reliable predictions. Despite these limitations, the computational landscape is rapidly evolving. An increasing number of open-source, commercial, and web-based platforms now support binding mode exploration, lead optimization, and structure-based VS of covalent ligands. Alongside traditional approaches, new artificial intelligence (AI) and machine learning (ML) tools are assisting in prioritizing candidate molecules. This review introduces the fundamental principles and mechanisms of covalent inhibition, then provides a comprehensive overview of computational tools including covalent docking, warhead placement algorithms, and pharmacophore modeling, supporting early-stage drug discovery and chemical biology. Case studies highlight practical applications. We also cover curated databases of covalent binders and experimental 3D protein-ligand complexes, plus tools for assessing nucleophilic residue reactivity, all essential for robust covalent modeling. Finally, we briefly address risks associated with covalent chemistry. While progress is notable, further advances are needed. Nonetheless, today's covalent docking and AI-driven tools already make a meaningful impact by enabling rational design, generating new ideas, refining hypotheses, and expanding the boundaries of druggability.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

SUMMARY: Several web-based tools predict the putative targets of a small molecule query compound by similarity to molecules with known bioactivity data using molecular fingerprints. In numerous situations, it would however be valuable to be able to run such computations on a local computer. We present FastTargetPred, a new program for the prediction of protein targets for small molecule queries. Structural similarity computations rely on a large collection of confirmed protein-ligand activities extracted from the curated ChEMBL 25 database. The program allows to annotate an input chemical library of ∼100k compounds within a few hours on a simple personal computer. AVAILABILITY AND IMPLEMENTATION: FastTargetPred is written in Python 3 (≥3.7) and C languages. Python code depends only on the Python Standard Library. The program can be run on Linux, MacOS and Windows operating systems. Pre-compiled versions are available at https://github.com/ludovicchaput/FastTargetPred. FastTargetPred is licensed under the GNU GPLv3. The program calls some scripts from the free chemistry toolkit MayaChemTools. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.