Protein–protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs

Adsorbate free energies are important parameters in surface chemistry and catalysis. Because of its simplicity, the harmonic oscillator (HO) model remains the most widely used method for calculating adsorbate free energy in many fields, including microkinetic modeling. However, it is well-known that the HO method is ineffective for weak adsorption. In this study, we propose a translational model with

This work introduces a dedicated thermostatization strategy for molecular dynamics simulations of gaseous systems. The proposed thermostat is based on the stochastic canonical velocity rescaling approach by Bussi and co-workers and is capable of ensuring an equal distribution of the kinetic energy among the translational, rotational, and vibrational degrees of freedom. The outlined framework ensures

Polyphosphate nucleotides, such as ATP, ADP, GTP, and GDP, play a crucial role in modulating protein functions through binding and/or catalytically activating proteins (enzymes). However, accurately calculating the binding free energies for these charged and flexible ligands poses challenges due to slow conformational relaxation and the limitations of force fields. In this study, we examine the accuracy

The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning

In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of

The long-held assumption that the optimization of parameters for NDDO-descendant semiempirical methods may be performed without precise geometry optimization is assessed in detail; the relevant equations for the analytical evaluation of the geometry-corrected derivatives of molecular properties that account for changes in the optimum geometry are then presented. The first and second derivatives calculated

The electronic structures of lanthanide monoxides (LnO/LnO+) and monosulfides (LnS/LnS+) for all lanthanide series elements (Ln = La-Lu) have been systematically analyzed with sophisticated quantum chemical calculations. The ground electronic configuration has been determined to be Ln 4fn6s1 or 4fn+1 for the neutral molecules and Ln 4fn for the cations. The low-lying energy states resulting from spin-orbit

Classical density functional theory (DFT) provides a versatile framework to study the polymers with complex topological structure. Generally, a classical DFT describes the excess Helmholtz free energy of nonbonded chain connectivity due to excluded-volume effects and electrostatic correlations using the first-order thermodynamic perturbation theory (referred to as DFT-TPT1). Beyond first-order perturbation

Inductive program synthesis (PS) has recently begun to emerge as a useful new approach to automatically generate algorithms in quantum chemistry, as demonstrated in recent applications to the vibrational Schrödinger equation for simple model systems with one or two degrees-of-freedom. Here, we report a new physics-informed approach to inductive PS that is more conducive to the generation of discrete

The design of drug molecules is a critical stage in the drug discovery process. The structure-based drug design has long played an important role in efficient development. Significant progress has been made in recent years in the generation of 3D molecules via deep generation models. However, while many existing models have succeeded in incorporating structural information on target proteins, they

Structure-based virtual screening (SBVS) plays an indispensable role in the early phases of drug discovery, utilizing computational docking techniques to predict interactions between molecules and biological targets. During the SBVS process, selecting appropriate target structures and screening algorithms is crucial, as these choices significantly shape the outcomes. Typically, such selections require

Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their

Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use

A challenge to materials discovery is the identification of the physical features that are most correlated to a given target material property without redundancy. Such variables necessarily comprise the optimal search domain in subsequent material design. Here, we introduce a reinforcement learning-based material model (ReLMM) as a tool for analyzing a given database in identifying a minimal or near

Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, more than 90% of RDs lack effective therapies. Traditional drug discovery models, marked by lengthy development cycles and high failure rates, struggle

The significant importance of GABAA receptors in the treatment of central nervous system (CNS) disorders has been known for a long time. However, only in recent years have experimental protein structures been published that can open the door to understanding protein-ligand interactions and may effectively help the rational drug design for the future. In our previous work (Szabó, G. J. Med. Chem. 2022

Chagas disease, caused by the protozoan Trypanosoma cruzi, remains a significant global health burden, particularly in Latin America, where millions are at risk. This disease predominantly affects socioeconomically vulnerable populations, aggravating economic inequality, marginalization, and low political visibility. Despite extensive research, effective treatments are still lacking, partly due to

Numerous studies show that circular RNA (circRNA) functions as a sponge for microRNA (miRNA), significantly regulating gene expression by interacting with miRNA, which in turn affects the progression of human diseases. Traditional experimental approaches for investigating circRNA-miRNA interactions (CMI) are both time-consuming and costly, making computational methods a valuable alternative. Hence

Due to their computational intensity, long-range corrections of three-body interactions are particularly desirable, while there is no consensus of how to devise a cutoff scheme. A cutoff correction scheme for three-body interactions in molecular simulations is proposed that does not rest on complex integrals and can be implemented straightforwardly. For a limited number of configurations, the three-body

The successful simulation of proteins by molecular dynamics (MD) critically depends on the accuracy of the applied force field. Here, we modify the AMBER-family ff99SBnmr2 force field through improvements to the side-chain χ1 dihedral angle potentials in a residue-specific manner using conformational dihedral angle distributions from an experimental coil library as targets. Based on significant deviations

Kohn-Sham (KS) density functional theory (DFT) is an extremely popular, in-principle exact method, which can describe any many-electron system by introducing an auxiliary system of noninteracting electrons with the same density. When the number of electrons, N, changes continuously, taking on both integer and fractional values, the density has to be piecewise-linear, with respect to N. In this article

In rational drug discovery, both free energy of binding and the binding half-life (koff) are important factors in determining the efficacy of drugs. Numerous computational methods have been developed to predict these important properties, many of which rely on molecular dynamics (MD) simulations. While binding free-energy methods (thermodynamic equilibrium predictions) have been well validated and

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein

Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and

It stands to reason that the amount and the quality of data are of key importance for setting up accurate artificial intelligence (AI)-driven models. Among others, a fundamental aspect to consider is the bias introduced during sample selection in database generation. This is particularly relevant when a model is trained on a specialized data set to predict a property of interest and then applied to

The concordant mode approach (CMA) is a promising new scheme for dramatically increasing the system size and level of theory achievable in quantum chemical computations of molecular vibrational frequencies. Here, we achieve advances in the CMA hierarchy by computations targeting CCSD(T)/cc-pVTZ (coupled cluster singles and doubles with perturbative triples using a correlation-consistent polarized-valence

Accurate calculation of solvation energies has long fascinated researchers, but complex interactions within bulk water molecules pose significant challenges. Currently, molecular solvation energy calculations are mostly based on implicit solvent approximations in which the solvent molecules are treated as continuum dielectric media. However, the implicit solvent approach is not ideal because it lacks

The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, misinterpretations in nonadiabatic dynamics caused by inaccurate underlying potentials are often difficult to foresee. This work focuses on revealing the systematic errors in the nonadiabatic simulations due to the underlying potentials and suggests a thrifty approach

Predicting the properties for unseen materials exclusively on the basis of the chemical formula before synthesis and characterization has advantages for research and resource planning. This can be achieved using suitable structure-free encoding and machine learning methods, but additional processing decisions are required. In this study, we compare a variety of structure-free materials encodings and

Macrocyclization is a critical strategy in rational drug design that can offer several advantages, such as enhancing binding affinity, increasing selectivity, and improving cellular permeability. Herein, we introduce MacGen, a web tool devised for structure-based macrocycle design. MacGen identifies exit vector pairs within a ligand that are suitable for cyclization and finds 3D linkers that can align

Although being able to determine whether a host molecule can enclose a guest molecule and form a caging complex could benefit numerous chemical and medical applications, the experimental discovery of molecular caging complexes has not yet been achieved at scale. Here, we propose MoleQCage, a simple tool for the high-throughput screening of host and guest candidates based on an efficient robotics-inspired

Proteins are dynamic entities that adopt diverse conformations, which play a pivotal role in their function. Understanding these conformations is essential, and protein collective motions, particularly those captured by normal mode (NM) and their linear combinations, provide a robust means for conformational sampling. This work introduces a novel approach to obtaining a uniformly oriented set of a

A composite method, named ANN-G3S, is introduced, adapting from G3S theory and employing distinct sets of multiplicative scale factors. An artificial neural network (ANN)-based classification model is utilized to select optimal sets of four scale factors for electronic correlation and basis set expansion terms in electronic systems. The correlation and basis set terms are scaled by four parameters