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The design of new host-guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host-guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host-guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.

Over the last decades, molecular simulations have spread through the drug discovery arena. This trend is expected to continue in the foreseeable future thanks to increased performance and the positive impact they can exert on productivity. In this article we highlight three aspects of molecular modelling for which we expect significant improvements over the next 25 years. Increased computational resources, faster algorithms and novel methods to sample rare events will provide a better handle on target flexibility and its relation with ligand binding. More accurate target druggability predictions will improve the success, but also broaden the scope of target-based drug discovery strategies. Finally, the use of higher levels of theory will increase the accuracy of protein-ligand binding affinity predictions, resulting in better hit identification success rates as well as more efficient lead optimization processes.

We investigate the changes in the solvation properties of the natural nucleic acid bases due to the formation of the canonical Watson-Crick hydrogen-bonded complexes. To this end, the changes in the free energy of solvation of the bases induced upon hydrogen-bonded dimerization are analyzed by means of the hydrophobic similarity index, which relies on the atomic contributions to the free energy of solvation determined by the partitioning method implemented in the framework of the MST continuum model. Such an index is also used to examine the hydrophobic similarity between the canonical nucleic acid bases and a series of highly apolar analogues, which have been designed as potential candidates to expand the genetic alphabet. The ability of these analogues to be incorporated into modified DNA duplexes can be related to the large reduction in the hydrophilicity of the natural bases upon formation of the canonical hydrogen-bonded dimers. The results illustrate the suitability of the hydrophobic similarity index to rationalize the role played by solvation in molecular recognition.

A new and rigorous method for the fractional description of solvation and transfer free energies is presented. The method is based on the use of the Miertus-Scrocco-Tomasi self-consistent reaction field method (MST-SCRF), and allows for a rigorous partition of the total solvation free energy into surface elements. The method gives a complete picture of the hydrophobicity/hydrophilicity of molecules. Present results allow us to expect that the method might provide useful information in drug design and molecular modeling studies.