Abstract
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.
Highlights
Supramolecular chemistry has experienced enormous growth in recent years
The Gibbs free energies of the optimized geometries were calculated as the sum of the Electronic Energy (E), which includes the D4 dispersion correction, thermostatistical corrections (GRRHOT) calculated following a coupled rigid-rotorharmonic-oscillator approach, and the solvation contribution (Gsolv) calculated by the implicit solvation model GBSA
We extract approximately 15 structures from the classical molecular dynamics simulations and carry out a geometric optimization at a semiempirical level, followed up by calculation of the hessian to confirm that the final energy is a true minimum
Summary
Supramolecular chemistry has experienced enormous growth in recent years. Supramolecular processes, and host–guest systems in particular, are studied both from a fundamental perspective and for their possible applications [1,2,3,4]. Quantitative predictions of binding free energies are difficult, but essential to guide the synthetic efforts, leading to more efficient design and discovery of host–guest systems with the desired activities. In this context, some of the tools currently used in computer-aided drug design (CADD) could be as useful for this endeavor, in the same way as they are for drug discovery [9,10,11,12]. Host–guest systems are orders of magnitude simpler (in terms of degrees of freedom) than biomolecular complexes and, because they are chemically stable, can be studied in a variety of well-controlled environmental conditions This offers an opportunity to test and
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