Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods

ACCELERATED COMPUTING FOR MOLECULAR DYNAMICS SIMULATION

Molecular dynamics (MD) simulation serves as a computational microscope into the behavior of the biological and chemical macromolecules. At its core, MD models the interactions between atoms at various levels – force fields model the higher quantum level interactions using simpler physics-based models of interaction energies, while periodic boundary conditions model the bulk phase using lattice-based periodic copies of the simulation box. One limitation of the finite size of the simulation box seen during the simulation of membrane bilayers is the artifact of a chemical disequilibrium between the two layers as a drug molecule enters into the bilayer. We have tried to solve this problem by using a periodic boundary condition which has a half screw symmetry. Our results show that the method scales similar to the best-known method for the normal periodic boundary conditions. We have migrated CHARMM to an efficient implementation on the GPUs. These architectures provide thousands of cores on the same chip but require different programming model in order to use the underlying architecture. Our results show that the new CHARMM CUDA engine is efficient in time and accurate in precision. We have also participated in blind prediction challenges organized by SAMPL community to have a fair assessment of the computational chemistry tools. We developed a hybrid QM and MM technique to predict the pKa of drug-like molecules. It avoids the implicit solvent model used by quantum mechanical models and uses explicit solvent molecules. Since modeling explicit solvent molecules is difficult at QM level, they are modeled at the MM level instead. Thermodynamic cycle couples the aqueous Gibbs free energy of deprotonation to simpler components which can be modeled with higher accuracy. We also built a deep learning model to predict the logP of a set of drug-like molecules in a blind fashion. The generated model is robust over a large number of molecules, not just the ones that it was tested for in the SAMPL competition. We expect the method to be interesting for the drug design industry since lipophilicity of a molecule is important to be known even before it has been synthesized

New molecular simulation methods for quantitative modelling of protein-ligand interactions

The main theme of this work is the design and development of new molecular simulation protocols, to achieve more accurate and reliable estimates of free energy changes for processes relevant to the structure-based drug design. The works starts with an insight into the reproducibility problem for alchemical free energy calculations. Even if simulations are run with similar input files, the use of different simulation engines could give different free energy results. As part of a collaborative effort, the implementation details of AMBER, GROMACS, SOMD and CHARMM simulation codes were studied and free energy protocols for each software were validated to converge towards a reproducibility limit of about 0.20 kcal.mol-1 for hydration free energies of small organic molecules. Following, new simulation methods for the estimation of lipophilicity coefficients (log P and log D) for drug like molecules were developed and validated. log P values were computed for a dataset of 5 molecules with increasing fluorination level. Predictions were in line with the experimental measures and the simulations also allowed new insights into the water-solute interactions that drive the partitioning process. Then, as part of the SAMPL5 challenge, log D values for 53 drug-like molecules were computed. In this context two different simulation models were derived in order to take into account the presence of protonated species. The results were encouraging but also highlighted limits in alchemical free energy modelling. As an additional task of the SAMPL5 contest, three different protocols were validated for predicting absolute binding affinities for 22 host-guest systems. The first model yielded a free energy of binding based on free energy changes in solvated and complex phase; the second added the long range dispersion correction to the previous model; the third one used a standard state correction term. All three protocols were among the top-ranked submission in SAMPL5, with a correlation coefficient R2 of about 0.7 against experimental data. Finally, the origins and magnitude of the finite size artefacts in alchemical free energy calculations were investigated. Finite size artefacts are especially predominant in calculations that involve changes in the net-charge of a solute. A new correction scheme was devised for the Barker Watts Reaction Field approach and compared with the literature. Hydration free energy calculations on simple ionic species were carried out to validate the consistency of the scheme and the approach was further extended to host-guest binding affinities predictions

Computational modelling of solvent effects

This thesis is concerned with developing theoretical benchmarks and computational procedures that would facilitate robust descriptions of solvent effects on molecular properties and chemical reactions. This advancement will enable chemists to design more effective chemical reagents, drug molecules and materials, thereby reducing the need for extensive experimental trial-and-error. Towards this end, this thesis has developed theoretical benchmarks to evaluate the performance of lower-cost and approximate methods in predicting solute-solvent interaction energies. This includes the generation of high-level calculations of solute-solvent interactions and proton transfer reaction energies in very large water clusters (up to 160 water molecules) at a variety of solute-solvent configurations. This differs from previous studies, which mostly focused on small solvated clusters (1-6 solvent molecules) at equilibrium geometries. These theoretical benchmarks were then used to assess the performance of a range of contemporary density functional theory methods and hybrid quantum mechanics/molecular mechanics (QM/MM) approximations of these methods. A surprising finding was that significantly larger than expected QM region size (solute plus 40 or more water molecules) was needed before the QM/MM models converged to within 5.7 kJ mol-1 of the direct QM result. To address this limitation, an important contribution of this thesis is the development of efficient strategies based on charge-shift analysis and electrostatically embedded fragment methods to accelerate the convergence of the QM/MM models with respect to QM region size. Of particular note, the QM region selection based on atomic charges significantly reduced the errors in QM/MM models even when a low-level embedding potential was used. Finally, these findings culminated in developing a dual-Hamiltonian approach that may be used to systematically improve the accuracy of force field explicit solvent simulations of barriers of organic reactions. It is envisaged that these developments will directly contribute to the development of a systematic framework for improving computational simulations of solution-phase processes

POMSimulator: A method for understanding the Multi-Equilibria and Self-Assembly Processes of Polyoxometalates

En aquesta tesi hem desenvolupat POMSimulator, una novetat computacional mètode que prediu l'especiació aquosa d'oxocúmuls moleculars així com el mecanisme de formació. En el nostre primer treball nosaltres va provar aquesta metodologia amb un sistema model: l'octamolibdat, [Mo8O26]4−.1 Hem informat dels diagrames d'especiació dels molibdats amb alta precisió, i vam proposar un mecanisme de reacció tenint en compte els equilibris àcid-base. A continuació, hem ampliat aquesta metodologia a tots els isopolioxomolibdats i -tungstats.2 Hem descrit el formació de cúmuls més grans com: [H32Mo36O128]8−, [W12O42]12−, [W12O40]8−, i [W10O32]4−. A més, hem introduït diagrames de fase en la nostra metodologia per obtenir una millor visió general de l'especiació a diferents concentracions. Recentment, hem aplicat POMSimulator als polioxovanadats, -niobats i tantlats.3 Hem demostrat que es podrien calcular constants de formació precises per als tres sistemes metall-oxo. Vam proposar un intermedi vanadat, [V5O14]3−, que podria estar implicada en la interconversió del decavanadat, [V10O28]6−, i els metavanadats, [VnO3n]n− (n=4,5,6). A més, vam informar del diagrama d'especiació dels niòbats, inclòs el primordial clústers com [H9Nb24O72]15− i [Nb7O22]9−. En conxiii trast, no hem trobat cap evidència de la formació dels anàlegs de tàntal. No obstant això, vam observar la formació del decatantalat, [Ta10O28]6−, tot i que encara no s'ha sintetitzat a causa de la seva poca solubilitat en aigua. En general, creiem que el nostre mètode ho pot fer trobar una sinergia molt prometedora amb la química experimental. xivEn esta tesis hemos desarrollado POMSimulator, un novedoso computacional método que predice la especiación acuosa de oxoclusters moleculares así como el mecanismo de formación. En nuestro primer trabajo nos probó esta metodología con un sistema modelo: el octamolibdato, [Mo8O26]4−.1 Reportamos los diagramas de especiación de molibdatos con alta precisión, y propusimos un mecanismo de reacción considerando los equilibrios ácido-base. A continuación, ampliamos esta metodología a todos los isopolioxomolibdatos y -tungstatos.2 Describimos el formación de cúmulos más grandes como: [H32Mo36O128]8−, [W12O42]12−, [W12O40]8-, y [W10O32]4-. Además, introdujimos los diagramas de fase. en nuestra metodología para obtener una mejor visión general de la especiación a diferentes concentraciones. Recientemente, hemos aplicado POMSimulator polioxovanadatos, -niobatos y tantalatos.3 Mostramos que las constantes de formación precisas podrían calcularse para los tres sistemas metal-oxo. Propusimos un intermedio de vanadato, [V5O14]3−, que podría estar involucrado en la interconversión del decavanadato, [V10O28]6−, y los metavanadatos, [VnO3n]n− (n=4,5,6). Es más, informamos el diagrama de especiación de los niobatos, incluido el primordial grupos como [H9Nb24O72]15- y [Nb7O22]9-. En conxiii contraste, no encontramos evidencia de la formación de los análogos de tantalio. No obstante, observamos la formación del decatantalato, [Ta10O28]6−, aunque aún no ha sido sintetizado debido a su poca solubilidad en agua. En general, creemos que nuestro método puede encontrar una sinergia muy prometedora con la química experimental. xivIn this thesis we have developed POMSimulator, a novel computational method that predicts the aqueous speciation of molecular oxoclusters as well as the formation mechanism. In our first work we tested this methodology with a model system: the octamolybdate, [Mo8O26]4−.1 We reported the speciation diagrams of molybdates with high accuracy, and we proposed a reaction mechanism considering the acid-base balance. Next, we extended this methodology to all the isopolyoxomolybdates and -tungstates.2 We described the formation of larger clusters such as: [H32Mo36O128]8−, [W12O42]12−, [W12O40]8−, and [W10O32]4−. Besides, we introduced phase diagrams in our methodology to obtain a better overview of the speciation at different concentrations. Recently, we have applied POMSimulator to polyoxovanadates, -niobates, and tantalates.3 We showed that accurate formation constants could be computed for the three metal-oxo systems. We proposed a vanadate intermediate, [V5O14]3−, which could be involved in the interconversion of the decavanadate, [V10O28]6−, and the metavanadates, [VnO3n]n− (n=4,5,6). Furthermore, we reported the speciation diagram of niobates including paramount clusters such as [H9Nb24O72]15− and [Nb7O22]9−. in conxiii trast, we found no evidence of the formation of the tantalum analogues. Nevertheless, we observed the formation of the decatantalate, [Ta10O28]6−, even though it has not been synthesized yet due to its poor solubility in water. Overall, we believe that our method can find a very promising synergy with experimental chemistry. xi

High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge

Recent advances in the development of low-cost quantum chemical methods have made the prediction of conformational preferences and physicochemical properties of medium-sized drug-like molecules routinely feasible, with significant potential to advance drug discovery. In the context of the SAMPL6 challenge, macroscopic pKa values were blindly predicted for a set of 24 of such molecules. In this paper we present two similar quantum chemical based approaches based on the high accuracy calculation of standard reaction free energies and the subsequent determination of those pKa values via a linear free energy relationship. Both approaches use extensive conformational sampling and apply hybrid and double-hybrid density functional theory with continuum solvation to calculate free energies. The blindly calculated macroscopic pKa values were in excellent agreement with the experiment