We introduce a method for reducing the number of valence states entering the calculation of screened the Coulomb interaction W in GW calculations. In this way, denoting with N the generic size of a system, the computational cost is brought from the typical O(N4) to the more favorable O(N2 ln N). The method becomes effective for large model structures. For enhancing the potentialities of our scheme

Comprehending water dynamics is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamic

Free energy perturbation (FEP) methodologies have become commonplace methods for modeling potency in hit-to-lead and lead optimization stages of drug discovery. The conformational states of the initial poses of compounds for FEP+ calculations are often set up by alignment to a cocrystal structure ligand, but it is not clear if this method provides the best result for all proteins or all ligands. Not

The acid dissociation constant (pKa), which quantifies the propensity for a solute to donate a proton to its solvent, is crucial for drug design and synthesis, environmental fate studies, chemical manufacturing, and many other fields. Unfortunately, the terminology used for describing acid-base phenomena is sometimes inconsistent, causing large potential for misinterpretation. In this work, we examine

Discovering new chemical compounds with specific properties can provide advantages for fields that rely on materials for their development, although this task comes at a high cost in terms of complexity and resources. Since the beginning of the data age, deep learning techniques have revolutionized the process of designing molecules by analyzing and learning from representations of molecular data,

Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper selection of atom-centered symmetry functions (ACSFs), also known as atomic features, to describe atomic environments. Inappropriate selection of ACSFs can lead to poor-quality ANN potentials. Here, we propose

Empirical parametrization underpins many scientific methodologies including certain quantum-chemistry protocols [e.g., density functional theory (DFT), machine-learning (ML) models]. In some cases, the fitting requires a large amount of data, necessitating the use of data obtained using low-cost, and thus low-quality, means. Here we examine the effect of using low-quality data on the resulting method

In recent years, machine learning has transformed many aspects of the drug discovery process, including small molecule design, for which the prediction of bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches but is fundamentally limited by the accuracy

The blood-brain barrier (BBB) selectively regulates the passage of chemical compounds into and out of the central nervous system (CNS). As such, understanding the permeability of drug molecules through the BBB is key to treating neurological diseases and evaluating the response of the CNS to medical treatments. Within the last two decades, a diverse portfolio of machine learning (ML) models have been

The binding of peptides to class-I Major Histocompability Complex (MHC) receptors and their subsequent recognition downstream by T-cell receptors are crucial processes for most multicellular organisms to be able to fight various diseases. Thus, the identification of peptide antigens that can elicit an immune response is of immense importance for developing successful therapies for bacterial and viral

Several computational tools have been developed to calculate sequence-based molecular descriptors (MDs) for peptides and proteins. However, these tools have certain limitations: 1) They generally lack capabilities for curating input data. 2) Their outputs often exhibit significant overlap. 3) There is limited availability of MDs at the amino acid (aa) level. 4) They lack flexibility in computing specific

Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of

Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening

Boron-doped graphene nanoribbons are promising platforms for developing organic materials with magnetic properties. Boron dopants can be used to create localized magnetic states in nanoribbons with tunable interactions. Controlling the coherence times of these magnetic states is the very first step in designing materials for quantum computation or information storage. In this work, we address the connection

In medicinal chemistry, compound optimization relies on the generation of analogue series (AS) for exploring structure-activity relationships (SARs). Potency progression is a critical criterion for advancing AS. During optimization, a key question is which analogues to synthesize next. We introduce a new computational methodology for the extension of AS with potent compounds containing both core structure

Global optimization of the structure of atomic nanoparticles is often hampered by the presence of many funnels on the potential energy surface. While broad funnels are readily encountered and easily exploited by the search, narrow funnels are more difficult to locate and explore, presenting a problem if the global minimum is situated in such a funnel. Here, a divide-and-conquer approach is applied

This study presents two new relativistic Gaussian basis sets without variational prolapse of double- and triple-ζ quality, RPF-2Z and RPF-3Z, along with augmented versions including additional diffuse functions, aug-RPF-2Z and aug-RPF-3Z, which are available for all s and p block elements from Hydrogen to Oganesson. The exponents of the Correlation/Polarization (C/P) functions are obtained from a polynomial

The N-representability problem consists in determining whether, for a given p-body matrix, there exists at least one N-body density matrix from which the p-body matrix can be obtained by contraction, that is, if the given matrix is a p-body reduced density matrix (p-RDM). The knowledge of all necessary and sufficient conditions for a p-body matrix to be N-representable allows the constrained minimization

A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing

Due to their efficient molecular design, nonfullerene acceptors (NFAs) have significantly advanced organic photovoltaics (OPVs). However, the lack of models to screen and evaluate candidate NFAs based on the resulting device performance has impeded the rapid development of high-performance molecules. This work introduces a computational framework utilizing a kinetic Monte Carlo (kMC) model to derive

A multiscale force field (FF) is developed for an aqueous solution of trivalent actinide cations An3+ (An = U, Np, Pu, Am, Cm, Bk, and Cf) by using a 12-6-4 Lennard-Jones type potential considering ion-induced dipole interaction. Potential parameters are rigorously and automatically optimized by the meta-multilinear interpolation parametrization (meta-MIP) algorithm via matching the experimental properties

A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated

The discovery of the three-dimensional shape of protein molecules using interatomic distance information from nuclear magnetic resonance (NMR) can be modeled as a discretizable molecular distance geometry problem (DMDGP). Due to its combinatorial characteristics, the problem is conventionally solved in the literature as a depth-first search in a binary tree. In this work, we introduce a new search

Coronavirus disease 2019 (COVID-19) is caused by a new, highly pathogenic severe-acute-respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects human cells through its transmembrane spike (S) glycoprotein. The receptor-binding domain (RBD) of the S protein interacts with the angiotensin-converting enzyme II (ACE2) receptor of the host cells. Therefore, pharmacological targeting of this interaction

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer

A gradual but steady tide in theoretical chemistry is favoring the exploration of atomic and molecular interactions through the dynamical forces perceived and exerted by the particles of a system. By integrating the quantum mechanical force operator over all the spin and all but one of the spatial coordinates of the electrons, the Ehrenfest force density field reveals these forces directly and is separable

The accurate identification of protein–ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years

Locating plausible transition paths and enhanced sampling of rare events are fundamental to understanding the functional dynamics of biomolecules. Here, a constraint-based constant advance replicas (CAR) formalism of reaction paths is reported for identifying the most probable transition path (MPTP) between two given states. We derive the temporal-integrated effective dynamics governing the projected

Vibrational non-Condon effects, which describe how molecular vibrational transitions are influenced by a system's rotational and translational degrees of freedom, are often overlooked in spectroscopy studies of biological macromolecules. In this work, we explore these effects in the modeling of infrared (IR) spectra for nucleic acids in the 1600-1800 cm-1 region. Through electronic structure calculations

There are various flows inside and outside cells in vivo. Nonequilibrium molecular dynamics (NEMD) simulation is a useful tool for understanding the effects of these flows on the dynamics of biomolecules. We propose an NEMD method to generate a Poiseuille-like flow between lipid bilayers. We extended the conventional equilibrium MD method to produce a flow by adding constant external force terms to

This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data set of 251 AAs, Leave-One-Out-Cross-Validation (LOOCV), and three distinct data splits. Our research employs the GFN2-xTB method, known for its robustness

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