Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data.

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low.

Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target.

Toward a quasiphase transition in the single-file chain of water molecules: Simple lattice model.

Recently, Ma et al. [Phys. Rev.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands.

Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network.

Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweight computational methods, thus promoting machine-learning approaches for processing huge pools of candidates. In this contribution, we present a graph-based deep neural network for prediction of protein-drug binding affinity and assess its predictive power under thorough testing conditions.

Ensembling machine learning models to boost molecular affinity prediction.

This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands is predicted against a single receptor - human thrombin, thus, the models consider ligand features only. However, the suggested approach can be repurposed for other receptors.

Toward efficient generation, correction, and properties control of unique drug-like structures.

Efficient design and screening of the novel molecules is a major challenge in drug and material design. This paper focuses on a multi-stage pipeline, in which several deep neural network models are combined to map discrete molecular representations into continuous vector space to later generate from it new molecular structures with desired properties. Here, the Attention-based Sequence-to-Sequence model is added to "spellcheck" and correct generated structures, while the oversampling in the continuous space allows generating candidate structures with desired distribution for properties and molecular descriptors, even for a small reference datasets.

Explicit water molecular dynamics study of the mobility of halide ions in presence of ionene oligocations.

We present explicit water molecular dynamics simulations of solutions of aliphatic 3,3- and 6,6-ionene oligocations neutralized with (i) fluoride, chloride, bromide, or iodide counterions, respectively, or (ii) with a 1:1 mixture of chloride and bromide anions in presence of a low molecular weight salt at 298 K. The SPC/E model was used to describe water molecules. Results of the simulation are presented in form of the pair distribution functions between various atoms on the ionene oligoion and counterions in solution.

Isothermal titration calorimetry and molecular dynamics study of ion-selectivity in mixtures of hydrophobic polyelectrolytes with sodium halides in water.

Aliphatic x,y-ionenes are polyelectrolytes in which x and y denote the numbers of methylene groups separating quaternary ammonium ions. They represent useful model substances for studying hydrophobic and charge effects in aqueous solutions. We used isothermal titration calorimetry to measure the enthalpies of mixing, ΔH(mix), of 3,3- and 6,6-ionene fluorides and bromides with low molecular weight salts (NaF, NaCl, NaBr, and NaI) at 298 K in water.