At present, the legendary magic bullet, i.e., a drug with high potency and selectivity toward a specific biological target, shares the spotlight with an emerging and alternative polypharmacology approach. Polypharmacology suggests that more effective drugs can be developed by specifically modulating multiple targets. It is generally thought that complex diseases such as cancer and central nervous system diseases may require complex therapeutic approaches. In this respect, a drug that "hits" multiple sensitive nodes belonging to a network of interacting targets offers the potential for higher efficacy and may limit drawbacks generally arising from the use of a single-target drug or a combination of multiple drugs. In this review, we will compare advantages and disadvantages of multitarget versus combination therapies, discuss potential drug promiscuity arising from off-target effects, comment on drug repurposing, and introduce approaches to the computational design of multitarget drugs.

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Molecular docking is the premier approach to structure-based virtual screening. While ligand posing is often successful, compound ranking using force field-based scoring functions remains difficult. Uncertainties associated with scoring often limit the ability to confidently identify new active compounds. In this study, we introduce an alternative approach to compound ranking. Rather than using scoring functions for final ranking, compounds are prioritized on the basis of computed 3D similarity to known crystallographic ligands. For different targets, it is shown that 3D similarity-based ranking consistently improves the enrichment of active compounds compared to ranking obtained using scoring functions, even if only a single crystallographic ligand is used as a reference. While the strategy is not applicable in cases where no cocrystal structure is available, it should be a promising alternative or complement to conventional scoring in many instances. Since ligand similarity calculations are used to rank docking poses, which are independently derived, the approach introduced herein also contributes to the integration of ligand- and structure-based computational screening methods.

Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of $$\Delta$$ Δ -learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, $$\Delta$$ Δ -AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function.