Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

Improvement of multi-task learning by data enrichment: application for drug discovery.

Multi-task learning in deep neural networks has become a topic of growing importance in many research fields, including drug discovery. However, applying multi-task learning poses new challenges in improving prediction performance. This study investigated the potential of training data enrichment to enhance multi-task model prediction quality in drug discovery. The study evaluated four scenarios with varying degrees of information capacity of the training data and applied two types of test data to evaluate prediction performance. We used three datasets: ViralChEMBL, which consisted of binary activities of compounds against viral species, was applied for the classification task; pQSAR(159) and pQSAR(4267), which consisted of bio-activities of compounds and assays from the research of the profile-QSAR method, were applied for regression tasks. We built multi-task models based on the feed-forward DNNs using the PyTorch framework. Our findings showed that training data enrichment could be an effective means of enhancing prediction performance in multi-task learning, but the degree of improvement depends on the quality of the training data. The more unique compounds and targets the training data included, the more new compound-target interactions are required for prediction improvement. Also, we found out that even using multi-task learning, one could not predict the interactions of compounds that are highly dissimilar from those used for model training. The study provides some recommendations for effectively employing multi-task learning in drug discovery to improve prediction accuracy and facilitate the discovery of novel drug candidates.

Baseline Model for Predicting Protein-Ligand Unbinding Kinetics through Machine Learning.

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.

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space.

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.

Interfacial structure and structural forces in mixtures of ionic liquid with a polar solvent.

Many applications of ionic liquids involve their mixtures with neutral molecular solvents. The chemical physics of these high-concentration electrolytes, in particular at interfaces, still holds many challenges. In this contribution we begin to unravel the relationship between measurements of structural ('solvation') forces in mixtures of ionic liquid with polar solvent and the corresponding structure determined by molecular dynamics simulations of the same mixtures. In order to make the quantitative link between experiments with mica surfaces and simulations with fixed-charge surfaces, we present an experimental procedure for determining the effective surface charge on mica in ionic liquid. We find that a structural cross-over recently inferred from force measurements appears to be supported by the simulations: at the cross-over, the charge-oscillatory structure switches to charge-monotonic, and solvent layering becomes dominant. Finally, we map out a phase diagram in composition-surface charge space delineating regions of charge-oscillatory interfacial structure and regions of charge-monotonic decay. We note that these features of structure and oscillatory forces are distinct from (acting simultaneously with) the recently reported longer range monotonic forces arising from anomalously long bulk screening lengths in high-concentration electrolytes.

The nanostructure of a lithium glyme solvate ionic liquid at electrified interfaces.

Solvate ionic liquids are a subclass of ionic liquids that have the potential to be used in a range of electrochemical devices. We present molecular dynamics simulations of the interfacial structure of thin films of one such lithium based solvate ionic liquid, [Li(G4)][TFSI], an equimolar solution of tetraglyme and lithium bistriflimide. This solvate ionic liquid is shown to form a novel interfacial structure at a plane electrode, which differs in a number of ways from the nanostructure observed for a conventional ionic liquid at similar interfaces. This paper explores the structural composition of the interfacial layers of this solvate ionic liquid, including their variation with surface charge, and the relation between chemical structure and interfacial arrangement.

Self-interaction error in DFT-based modelling of ionic liquids.

The modern computer simulations of potential green solvents of the future, involving the room temperature ionic liquids, heavily rely on density functional theory (DFT). In order to verify the appropriateness of the common DFT methods, we have investigated the effect of the self-interaction error (SIE) on the results of DFT calculations for 24 ionic pairs and 48 ionic associates. The magnitude of the SIE is up to 40 kJ mol(-1) depending on the anion choice. Most strongly the SIE influences the calculation results of ionic associates that contain halide anions. For these associates, the range-separated density functionals suppress the SIE; for other cases, the revPBE density functional with dispersion correction and triple-ζ Slater-type basis is suitable for computationally inexpensive and reasonably accurate DFT calculations.

Molecular origin of high free energy barriers for alkali metal ion transfer through ionic liquid-graphene electrode interfaces.

In this work we study mechanisms of solvent-mediated ion interactions with charged surfaces in ionic liquids by molecular dynamics simulations, in an attempt to reveal the main trends that determine ion-electrode interactions in ionic liquids. We compare the interfacial behaviour of Li(+) and K(+) at a charged graphene sheet in a room temperature ionic liquid, 1-butyl-3-methylimidazolium tetrafluoroborate, and its mixtures with lithium and potassium tetrafluoroborate salts. Our results show that there are dense interfacial solvation structures in these electrolytes that lead to the formation of high free energy barriers for these alkali metal cations between the bulk and direct contact with the negatively charged surface. We show that the stronger solvation of Li(+) in the ionic liquid leads to the formation of significantly higher interfacial free energy barriers for Li(+) than for K(+). The high free energy barriers observed in our simulations can explain the generally high interfacial resistance in electrochemical storage devices that use ionic liquid-based electrolytes. Overcoming these barriers is the rate-limiting step in the interfacial transport of alkali metal ions and, hence, appears to be a major drawback for a generalised application of ionic liquids in electrochemistry. Some plausible strategies for future theoretical and experimental work for tuning them are suggested.

Molecular dynamics simulation of the structure and interfacial free energy barriers of mixtures of ionic liquids and divalent salts near a graphene wall.

A molecular dynamics study of mixtures of 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIm][BF4]) with magnesium tetrafluoroborate (Mg[BF4]2) confined between two parallel graphene walls is reported. The structure of the system is analyzed by means of ionic density profiles, lateral structure of the first layer close to the graphene surface and angular orientations of imidazolium cations. Free energy profiles for divalent magnesium cations are calculated using two different methods in order to evaluate the height of the potential barriers near the walls, and the results are compared with those of mixtures of the same ionic liquid and a lithium salt (Li[BF4]). Preferential adsorption of magnesium cations is analyzed using a simple model and compared to that of lithium cations, and vibrational densities of states are calculated for the cations close to the walls analyzing the influence of the graphene surface charge. Our results indicate that magnesium cations next to the graphene wall have a roughly similar environment to that in the bulk. Moreover, they face higher potential barriers and are less adsorbed on the charged graphene walls than lithium cations. In other words, magnesium cations have a more stable solvation shell than lithium ones.

Salting-out effects by pressure-corrected 3D-RISM.

We demonstrate that using a pressure corrected three-dimensional reference interaction site model one can accurately predict salting-out (Setschenow's) constants for a wide range of organic compounds in aqueous solutions of NaCl. The approach, based on classical molecular force fields, offers an alternative to more heavily parametrized methods.

Fast and General Method To Predict the Physicochemical Properties of Druglike Molecules Using the Integral Equation Theory of Molecular Liquids.

We report a method to predict physicochemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D reference interaction site model of the integral equation theory of molecular liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark data sets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction.

Communication: Accurate hydration free energies at a wide range of temperatures from 3D-RISM.

We present a new model for computing hydration free energies by 3D reference interaction site model (3D-RISM) that uses an appropriate initial state of the system (as suggested by Sergiievskyi et al.). The new adjustment to 3D-RISM theory significantly improves hydration free energy predictions for various classes of organic molecules at both ambient and non-ambient temperatures. An extensive benchmarking against experimental data shows that the accuracy of the model is comparable to (much more computationally expensive) molecular dynamics simulations. The calculations can be readily performed with a standard 3D-RISM algorithm. In our work, we used an open source package AmberTools; a script to automate the whole procedure is available on the web (https://github.com/MTS-Strathclyde/ISc).

Probing the neutral graphene-ionic liquid interface: insights from molecular dynamics simulations.

We study basic mechanisms of the interfacial layer formation at the neutral graphite monolayer (graphene)-ionic liquid (1,3-dimethylimidazolium chloride, [dmim][Cl]) interface by fully atomistic molecular dynamics simulations. We probe the interface area by a spherical probe varying the charge (-1e, 0, +1e) as well as the size of the probe (diameter 0.50 nm and 0.38 nm). The molecular modelling results suggest that: there is a significant enrichment of ionic liquid cations at the surface. This cationic layer attracts Cl(-) anions that leads to the formation of several distinct ionic liquid layers at the surface. There is strong asymmetry in cationic/anionic probe interactions with the graphene wall due to the preferential adsorption of the ionic liquid cations at the graphene surface. The high density of ionic liquid cations at the interface adds an additional high energy barrier for the cationic probe to come to the wall compared to the anionic probe. Qualitatively the results from probes with diameter 0.50 nm and 0.38 nm are similar although the smaller probe can approach closer to the wall. We discuss the simulation results in light of available experimental data on the interfacial structure in ionic liquids.

Biphenyl scaffold for the design of NMDA-receptor negative modulators: molecular modeling, synthesis, and biological activity.

NMDA (N-methyl-d-aspartate) receptor antagonists are promising tools for the treatment of a wide variety of central nervous system impairments including major depressive disorder. We present here the activity optimization process of a biphenyl-based NMDA negative allosteric modulator (NAM) guided by free energy calculations, which led to a 100 times activity improvement (IC50 = 50 nM) compared to a hit compound identified in virtual screening. Preliminary calculation results suggest a low affinity for the human ether-a-go-go-related gene ion channel (hERG), a high affinity for which was earlier one of the main obstacles for the development of first-generation NMDA-receptor negative allosteric modulators. The docking study and the molecular dynamics calculations suggest a completely different binding mode (ifenprodil-like) compared to another biaryl-based NMDA NAM EVT-101.

Multigrid solver for the reference interaction site model of molecular liquids theory.

In this article, we propose a new multigrid-based algorithm for solving integral equations of the reference interactions site model (RISM). We also investigate the relationship between the parameters of the algorithm and the numerical accuracy of the hydration free energy calculations by RISM. For this purpose, we analyzed the performance of the method for several numerical tests with polar and nonpolar compounds. The results of this analysis provide some guidelines for choosing an optimal set of parameters to minimize computational expenses. We compared the performance of the proposed multigrid-based method with the one-grid Picard iteration and nested Picard iteration methods. We show that the proposed method is over 30 times faster than the one-grid iteration method, and in the high accuracy regime, it is almost seven times faster than the nested Picard iteration method.

Toward a universal model to calculate the solvation thermodynamics of druglike molecules: the importance of new experimental databases.

We demonstrate that a new free energy functional in the integral equation theory of molecular liquids gives accurate calculations of hydration thermodynamics for druglike molecules. The functional provides an improved description of excluded volume effects by incorporating two free coefficients. When the values of these coefficients are obtained from experimental data for simple organic molecules, the hydration free energies of an external test set of druglike molecules can be calculated with an accuracy of about 1 kcal/mol. The 3D RISM/UC method proposed here is easily implemented using existing computational software and allows in silico screening of the solvation thermodynamics of potential pharmaceutical molecules at significantly lower computational expense than explicit solvent simulations.

Self-assembly of trehalose molecules on a lysozyme surface: the broken glass hypothesis.

To help understand how sugar interactions with proteins stabilise biomolecular structures, we compare the three main hypotheses for the phenomenon with the results of long molecular dynamics simulations on lysozyme in aqueous trehalose solution (0.75 M). We show that the water replacement and water entrapment hypotheses need not be mutually exclusive, because the trehalose molecules assemble in distinctive clusters on the surface of the protein. The flexibility of the protein backbone is reduced under the sugar patches supporting earlier findings that link reduced flexibility of the protein with its higher stability. The results explain the apparent contradiction between different experimental and theoretical results for trehalose effects on proteins.

Salting out in organic solvents: a new route to carbon nanotube bundle engineering.

In this study we investigate salt effects on bundle formation of carbon nanotubes (CNTs) dispersed in an organic solvent, N-methyl-2-pyrrolidone (NMP). Addition of NaI salt leads to self-assembly of CNTs into well-recognizable bundles. It is possible to control the size of the CNT bundles by varying the salt concentration.