Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit.

Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.

Infinite Dilution Activity Coefficients as Constraints for Force Field Parametrization and Method Development.

Molecular simulations begin with an underlying energy model or force field and from this can predict diverse physical properties. However, force fields were often developed with relatively limited data sets, yet accuracy for diverse properties across a broad chemical space is desirable; therefore, tests of such accuracy are particularly important. Here, to this end, we calculated 237 infinite dilution activity coefficients (IDACs), comparing with experimental values from NIST's ThermoML database. We found that calculated IDAC values correlate strongly with experiment (Pearson R of 0.92 ± 0.01) and allow us to identify specific functional groups that appear to present challenges to the force field employed. One potentially valuable aspect of IDACs, as compared to solvation free energies, which have been frequently employed as force field tests, is that the same molecules serve both as solutes and solvents in different cases, allowing us to ensure that force fields are not overly tuned to one particular environment or solvent.

Reproducibility of Free Energy Calculations across Different Molecular Simulation Software Packages.

Alchemical free energy calculations are an increasingly important modern simulation technique to calculate free energy changes on binding or solvation. Contemporary molecular simulation software such as AMBER, CHARMM, GROMACS, and SOMD include support for the method. Implementation details vary among those codes, but users expect reliability and reproducibility, i.e., for a given molecular model and set of force field parameters, comparable free energy differences should be obtained within statistical bounds regardless of the code used. Relative alchemical free energy (RAFE) simulation is increasingly used to support molecule discovery projects, yet the reproducibility of the methodology has been less well tested than its absolute counterpart. Here we present RAFE calculations of hydration free energies for a set of small organic molecules and demonstrate that free energies can be reproduced to within about 0.2 kcal/mol with the aforementioned codes. Absolute alchemical free energy simulations have been carried out as a reference. Achieving this level of reproducibility requires considerable attention to detail and package-specific simulation protocols, and no universally applicable protocol emerges. The benchmarks and protocols reported here should be useful for the community to validate new and future versions of software for free energy calculations.

Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules.

Background: Solubility is a physical property of high importance to the pharmaceutical industry, the prediction of which for potential drugs has so far been a hard task. We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating the absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule. Methods: Chemical potentials were estimated from all-atom molecular dynamics simulations.  We used the Einstein molecule method (EMM) to predict the absolute chemical potential of the solid and solvation free energy calculations to predict the excess chemical potentials of the liquid-phase systems. Results: Reliable estimations of the chemical potentials for the solid and for a single ASA molecule using the EMM required an extremely large number of intermediate states for the free energy calculations, meaning that the calculations were extremely demanding computationally. Despite the computational cost, however, the computed value did not agree well with the experimental value, potentially due to limitations with the underlying energy model. Perhaps better values could be obtained with a better energy model; however, it seems likely computational cost may remain a limiting factor for use of this particular approach to solubility estimation.    Conclusions: Solubility prediction of drug-like solids remains computationally challenging, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.