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, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug–target, target–disease, and ultimately drug–disease associations.

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High-throughput docking is an established computational screening approach in drug design. This methodology enables a rapid identification of biologically active hit compounds, providing an efficient and cost-effective complement or alternative to experimental high-throughput screenings. However, limitations inherent to the methodology make docking results inevitably approximate. Two major Achille's heels include the use of approximated scoring functions and the limited sampling of the ligand-target complexes. Therefore, docking results require careful evaluation and further post-docking analyses. In this article, we will overview our approach to post-docking analysis in virtual screenings. BEAR (Binding Estimation After Refinement) was developed as a post-docking processing tool that refines docking poses by means of molecular dynamics (MD) and then rescores the ligands based on more accurate scoring functions (MM-PB(GB)SA). The tool has been validated and used prospectively in drug discovery applications. Future directions regarding refinement and rescoring in virtual screening are discussed.

Since the approval of three hydroxamic acid-based HDAC inhibitors as anticancer drugs, such functional groups acquired even more notoriety in synthetic medicinal chemistry. The ability of hydroxamic acids (HAs) to chelate metal ions makes this moiety an attractive metal binding group—in particular, Fe(III) and Zn(II)—so that HA derivatives find wide applications as metalloenzymes inhibitors. In this minireview, we will discuss the most relevant features concerning hydroxamic acid derivatives. In a first instance, the physicochemical characteristics of HAs will be summarized; then, an exhaustive description of the most relevant methods for the introduction of such moiety into organic substrates and an overview of their uses in medicinal chemistry will be presented.