Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Novel 3-substituted coumarins inspire a custom pharmacology prediction pipeline: an anticancer discovery adventure.

Aim: This research aims to expand the established pharmacological space of tumor-associated carbonic anhydrases (TACAs) by exploring the synthetically accessible chemical space of 3-substituted coumarins, with the help of in silico pharmacology prediction.Materials & methods: 52 novel 3-substituted coumarins were sketched, prioritizing synthetic feasibility. Their pharmacological potentials were estimated using a custom machine-learning approach. 17 compounds were predicted as cytotoxic against HeLa cells by interfering with TACAs. Those compounds were synthesized and biologically tested against HeLa cells. The three most potent compounds were assayed against multiple carbonic anhydrases, and their enzyme binding mechanism(s) were studied using molecular docking.Results: Experimental results exhibited pronounced consensus with in silico pharmacology predictions.Conclusion: Novel 3-substituted coumarins are herein dispatched to the cancer therapeutics space.

[Box: see text].

Remapping the Chemical Space and the Pharmacological Space of Drugs: What Can We Expect from the Road Ahead?

This work examines the current landscape of drug discovery and development, with a particular focus on the chemical and pharmacological spaces. It emphasizes the importance of understanding these spaces to anticipate future trends in drug discovery. The use of cheminformatics and data analysis enabled in silico exploration of these spaces, allowing a perspective of drugs, approved drugs after 2020, and clinical candidates, which were extracted from the newly released ChEMBL34 (March 2024). This perspective on chemical and pharmacological spaces enables the identification of trends and areas to be occupied, thereby creating opportunities for more effective and targeted drug discovery and development strategies in the future.

Current computational methods for predicting protein interactions of natural products.

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.