Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug–target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug–target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand–target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods’ utility in discovering novel drug candidates.

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation’s role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Promising promiscuity: Guest editors Maria Laura Bolognesi and Andrea Cavalli set the stage for this ChemMedChem Special Issue on Polypharmacology and Multitarget Drugs. They highlight cases in which a departure from the “one target, one drug” paradigm has proven advantageous, with single molecules that can engage two or more targets to address a given pathology. Articles in this issue underscore the importance of focusing ever more attention to polypharmacology in drug discovery efforts.

The signal transducer and activator of transcription (STAT) 1 protein plays a key role in the immune response against viruses and other pathogens by transducing, in the nucleus, the signal from type I, type II and type III IFNs. STAT1 activates the transcription of hundreds of genes, some of which have been well characterized for their antiviral properties. STAT1 gene deletion in mice and complete STAT1 deficiency in humans both cause rapid death from severe infections. STAT1 plays a key role in the immunoglobulin class-switch recombination through the upregulation of T-bet; it also plays a key role in the production of T-bet+ memory B cells that contribute to tissue-resident humoral memory by mounting an IgG response during re-infection. Considering the key role of STAT1 in the antiviral immune response, many viruses, including dangerous viruses such as Ebola and SARS-CoV-2, have developed different mechanisms to inhibit this transcription factor. The search for drugs capable of targeting the viral proteins implicated in both viral replication and IFN/STAT1 inhibition is important for the treatment of the most dangerous viral infections and for future viral pandemics, as shown by the clinical results obtained with Paxlovid in patients infected with SARS-CoV-2.

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.