Derivation of structure–kinetics relationships can help rational design and development of new small-molecule drug candidates with desired residence times. Efforts are now being directed toward the ...development of efficient computational methods. Currently, there is a lack of solid, high-throughput binding kinetics prediction approaches on bigger datasets. We present a prediction method for binding kinetics based on the machine learning analysis of protein–ligand structural features, which can serve as a baseline for more sophisticated methods utilizing molecular dynamics (MD). We showed that the random forest algorithm is capable of learning the protein binding site secondary structure and backbone/side-chain features to predict the binding kinetics of protein–ligand complexes but still with inferior performance to that of MD-based descriptor analysis. MD simulations had been applied to a limited number of targets and a series of ligands in terms of kinetics analysis, and we believe that the developed approach may guide new studies. The method was trained on a newly curated database of 501 protein–ligand unbinding rate constants, which can also be used for testing and training the binding kinetics prediction models.
We study the effects of ion size asymmetry and short-range correlations on the electrical double layer in ionic liquids: we perform molecular dynamics simulations of a model ionic liquid between two ...“electrodes” and calculate the differential capacitance of each as a function of the electrode potential. The capacitance curve has an asymmetric “bell-shape” character, in qualitative agreement with recent experiments and the mean-field theory (MFT) which takes into account the limitation on the maximal local density of ions. The short-range ionic correlations, not included in the MFT, lead to an overscreening effect which changes radically the structure of the double layer at small and moderate charging. With the radius of cations taken to be twice as large as anions, the position of the main capacitance maximum is shifted positively from the potential of zero charge (PZC), as predicted by MFT. An extension of the theory (EMFT), however, reproduces the simulated capacitance curve almost quantitatively. Capacitance curves for real ionic liquids will be affected by nonspherical shape of ions and sophisticated pair potentials, varying from liquid to liquid. But understanding the capacitance behavior of such model system is a basis for rationalizing those more specific features.
Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented ...by different end points: it can be measured for different species using different types of administration, etc., and it is questionable if the knowledge transfer between end points is possible. We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS). We demonstrated that multitask modeling provides significant improvement over single-output models and other machine learning methods. Our research reveals that multitask learning can be very useful to improve the quality of acute toxicity modeling and raises a discussion about the usage of multitask approaches for regulation purposes. Our MultiTox models are freely available in OCHEM platform (ochem.eu/multitox) under CC-BY-NC license.
In order to understand basic principles of the double layer formation in room temperature ionic liquids, we have performed Molecular Dynamic simulations for a simplified system: dense assembly of ...charged Lennard-Jones spheres between charged walls. For simplicity, in this first investigation we have considered the cations and anions of the same size. We have calculated the corresponding values of the double layer capacitance as a function of the electrode potential and compared the results with existing theories. We have found that the capacitance curve does not follow the U-shape of the Gouy–Chapman theory, but has a bell-shape in agreement with the mean-field theory that takes into account the effect of limited maximum packing of ions. The wings of capacitance decrease inversely proportional to the square root of the electrode potential, as prescribed by the mean-field theory and the charge conservation law at large electrode polarizations. We have found, however, that the mean-field theory does not quantitatively reproduce the simulation results at small electrode potentials, having detected their remarkable overscreening effects (ionic correlations). The plots for the distributions of ions near the electrode at different electrode charges show that for the considered system, unlike it is often assumed, the double layer is
not one layer thick. The overscreening effects, dominating near the potential of zero charge (p.z.c.), are suppressed by the high electrode polarizations, following the onset of the so-called ‘lattice saturation effect’. The maximum of the capacitance coincides with the p.z.c., but it is true only for this ‘symmetric’ system. If sizes of cations and anions are different the maximum will be shifted away from the p.z.c., and generally the shape of the capacitance curve could be more complicated.
We developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is ...comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.
Ionic liquids (ILs) are promising electrolytes for electrochemical applications due to their remarkable stability and high charge density. Molecular dynamics simulations are essential for a better ...understanding of the complex phenomena occurring at the electrode–IL interface. In this work, we have studied the interface between graphene and 1-ethyl-3-methyl-imidazolium tetrafluoroborate IL by density functional theory-based molecular dynamics (DFT-MD) simulations at variable surface charge densities. We have disassembled the electrical double layer potential drop into two main components: one involving atomic charges and the other dipoles. The former component arises due to the reorganization of ionic liquid and the latter due to the electronic polarization of the surface. It is related to concepts hotly debated in the literature, such as the Thomas–Fermi screening length, effective surface charge plane, and quantum capacitance.
We present a theoretical/computational framework for accurate calculation of hydration free energies of ionized molecular species. The method is based on a molecular theory, 3D-RISM, combined with a ...recently developed pressure correction (PC+). The 3D-RISM/PC+ model can provide ∼3 kcal/mol hydration free energy accuracy for a large variety of ionic compounds, provided that the Galvani potential of water is taken into account. The results are compared with direct atomistic simulations. Several methodological aspects of hydration free energy calculations for charged species are discussed.
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