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.

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.

Exploring Chemical Reaction Space with Reaction Difference Fingerprints and Parametric t-SNE.

Humans prefer visual representations for the analysis of large databases. In this work, we suggest a method for the visualization of the chemical reaction space. Our technique uses the t-SNE approach that is parameterized using a deep neural network (parametric t-SNE).

Transformer-based artificial neural networks for the conversion between chemical notations.

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.

How risky is it to visit a supermarket during the pandemic?

We performed large-scale numerical simulations using a composite model to investigate the infection spread in a supermarket during a pandemic. The model is composed of the social force, purchasing strategy and infection transmission models. Specifically, we quantified the infection risk for customers while in a supermarket that depended on the number of customers, the purchase strategies and the physical layout of the supermarket.

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.

Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.

Background: Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.

graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes.

In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant ( ), inhibition constant ( ), and half maximal inhibitory concentration (IC). Being rigorously trained on the PDBbind dataset, the model achieves the Pearson correlation coefficient of 0.

Computational characterization of the glutamate receptor antagonist perampanel and its close analogs: density functional exploration of conformational space and molecular docking study.

Perampanel approved by FDA in 2012 is a first-in-class antiepileptic drug which inhibits α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor currents. It is markedly more active than many of its close analogs, and the reasons for this activity difference are not quite clear. Recent crystallographic studies allowed the authors to identify the location of its binding site.

Chemical space exploration guided by deep neural networks.

A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem. It is able to retain more information than certain dimensionality reduction techniques used for this purpose (principal component analysis (PCA), multidimensional scaling (MDS)). The applicability of this method to some chemical space navigation tasks (activity cliffs and activity landscapes identification) is discussed.

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.

3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction.

In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called three-dimensional reference interaction site model.

Luminescence in germania-silica fibers in a 1-2 μm region.

We analyze the origins of the luminescence in germania-silica fibers with high germanium concentration (about 30 mol.% GeO) in the region of 1-2 μm with a laser pump at the wavelength 532 nm. We show that such fibers demonstrate the high level of luminescence which unlikely allows the observation of photon triplets, generated in a third-order spontaneous parametric downconversion process in such fibers.

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.

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.

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][BF]) with magnesium tetrafluoroborate (Mg[BF]) 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[BF]).

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.

Molecular dynamics simulation of the behaviour of water in nano-confined ionic liquid-water mixtures.

This work describes the behaviour of water molecules in 1-butyl-3-methylimidazolium tetrafluoroborate ionic liquid under nanoconfinement, between graphene sheets. By means of molecular dynamics simulations, the adsorption of water molecules at the graphene surface is studied. A depletion of water molecules in the vicinity of the neutral and negatively charged graphene surfaces, and their adsorption at the positively charged surface are observed in line with the preferential hydration of the ionic liquid anions.

Predicting Solvation Free Energies Using Parameter-Free Solvent Models.

We present a new approach for predicting solvation free energies in nonaqueous solvents. Utilizing the corresponding states principle, we estimate solvent Lennard-Jones parameters directly from their critical points. Combined with atomic solutes and the pressure corrected three-dimensional reference interaction site model (3D-RISM/PC+), the model gives accurate predictions for a wide range of nonpolar solvents, including olive oil.

[Period-tripling in Multiscale Physical and Biological Events].

A recent paper by S.J. Puetz et al.

Hydration Free Energies of Molecular Ions from Theory and Simulation.

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.

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.

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.