Communications Chemistry - Automated free energy calculations for the prediction of binding free energies of ligands to a protein target are gaining importance for drug discovery, but building...

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated...

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine...

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein–li...

Accurate in silico prediction of protein–ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional...

A pervasive challenge in drug design is determining how to expand a ligand─a small molecule that binds to a target biomolecule─in order to improve various properties of the ligand. Adding single...

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine...

We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally...

A self-guided Colab tutorial about machine learning potential for reactive systems are presented in this work. The tutorial begins with the introduction of feedforward neural network and kernel-bas...

The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structu...

Nature Machine Intelligence - Deep learning generative approaches have been used in recent years to discover new molecules with drug-like properties. To improve the performance of such approaches,...

Recent advances in machine learning methods have had a significant impact on protein structure prediction, but accurate generation and characterization of protein-folding pathways remains intractab...

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML)....

Nature Machine Intelligence - Drug design has recently seen immense improvements in computational methods, but models can still struggle generalizing across binding pockets. Feng and colleagues...

Nature Reviews Chemistry - Quantum Monte Carlo methods using neutral-network ansatzes can provide virtually exact solutions to the electronic Schrödinger equations for small systems and are...

Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures...

Nature Machine Intelligence - Generative models for chemical structures are often trained to create output in the common SMILES notation. Michael Skinnider shows that training models with the goal...

Molecular simulations, which simulate the motions of particles according to fundamental laws of physics, have been applied to a wide range of fields from physics and materials science to biochemistry...

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of...

Nature Machine Intelligence - Machine learning techniques are widely employed in chemical science, but are application specific and their development requires dedicated expertise. Jablonka and...

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing...

Nature - Recent advances in computational approaches and challenges in their application to streamlining drug discovery are discussed.

Predicting the binding affinity of protein–ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring...

Molecular dynamics simulations produce trajectories that correspond to vast amounts of structure when exploring biochemical processes. Extracting valuable information, e.g., important intermediate...

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind’s end-to-end model, AlphaFold2, has demonstrated the...

Nature Communications - Predicting protein interactions is crucial for understanding biological functions. Here, authors introduce a geometric transformer that accurately identifies protein binding...

This letter gives results on improving protein–ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and...

Nature Computational Science - Chemical reaction networks are widely used to examine the behavior of chemical systems. While diverse strategies exist for chemical reaction network construction and...

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projec...

Rapid computational exploration of the free energy landscape of biological molecules remains an active area of research due to the difficulty of sampling rare state transitions in molecular dynamics...

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning...

Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug metabolism in the human body, and CYP activity toward compounds can significantly affect druggability, making early prediction of...

Nature Communications - The use of data-driven generative models for drug design is challenging due to the scarcity of data. Here, the authors introduce a “zero-shot" generative deep...

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of...

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows...

Nature Communications - Structure-based generative chemistry is crucial in computer-aided drug discovery. Here, authors propose PMDM, a conditional generative model for 3D molecule generation...

Protein–ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking...

We present a novel strategy to explore conformational changes and identify stable states of molecular objects, eliminating the need for a priori knowledge. The approach applies a deep learning method...

Nature Communications - Chemical structures are typically published as nonmachine-readable images in scientific literature. Here, the authors present DECIMER.ai, an open platform for translating...

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To...

Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry...

Nature Machine Intelligence - Contact prediction between two proteins is still computationally challenging, but is vital for understanding multi-protein complexes. Lin et al. use a geometric deep...

We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description...

Nature Machine Intelligence - Graph neural networks have proved useful in modelling proteins and their ligand interactions, but it is not clear whether the patterns they identify have biological...

Many research questions benefit from molecular dynamics simulations to observe the motions and conformations of molecules over time, which rely on force fields that describe sets of common molecules...

Communications Chemistry - Chemical variational autoencoder (VAE) is a deep learning method for constructing chemical latent spaces, however, the current chemical VAE variants are limited in their...

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule...

Data science is becoming a mainstay in research. Despite this, very few STEM graduates matriculate with basic formal training in programming. The current lesson plan was developed to introduce...

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small...

Nature Computational Science - A deep-learning model, DetaNet, is proposed to efficiently and precisely predict molecular scalars, vectorial and tensorial properties, as well as the infrared,...

Flavor is an essential component in the development of numerous products in the market. The increasing consumption of processed and fast food and healthy packaged food has upraised the investment in...

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances...

Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called "de...

Nature Machine Intelligence - AI methods can discover new antibiotics but existing methods have limitations. Swanson et al. develop a generative AI model that learns to design molecules that are...

As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the...

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdam...

Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely...

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first...

Nature Computational Science - A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug...

Understanding the intricate phenomenon of neuronal wiring in the brain is of great interest in neuroscience. In the fruit fly, Drosophila melanogaster, the Dpr-DIP interactome has been identified to...

Small-molecule conformer generation (SMCG) is an extremely important task in both ligand- and structure-based computer-aided drug design, especially during the hit discovery phase. Recently, a...

Language modeling has seen impressive progress over the last years, mainly prompted by the invention of the Transformer architecture, sparking a revolution in many fields of machine learning, with...

Nature Chemistry - Atomistic simulations have a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient...

Rational structure-based drug design relies on accurate predictions of protein–ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting...

High-quality protein–ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However,...

Classification of molecular structures is a crucial step in molecular dynamics (MD) simulations to detect various structures and phases within systems. Molecular structures, which are commonly...

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models...

The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small in comparison...

Despite advances in artificial intelligence methods, protein folding remains in many ways an enigma to be solved. Accurate computation of protein folding energetics could help drive fields such as...

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure–funct...

Generating molecules, both in a directed and undirected fashion, is a huge part of the drug discovery pipeline. Genetic algorithms (GAs) generate molecules by randomly modifying known molecules....

Flexible modeling of the protein–ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-...

This study introduces EvoMPF, a general methodology to encode molecular structures for machine learning. Addressing the challenge of adapting representations for specific chemical questions, the...

Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization...

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often...

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consum...

Nature Machine Intelligence - Generating novel molecules that bind to specific protein targets is a challenging but important task in computational drug design. Zhang and colleagues present a...

Artificial intelligence (AI) in the form of deep learning shows great promise for drug discovery, from predicting protein structures to designing ligands de novo. AI for structure-based drug discov...

A drug discovery and development pipeline is a prolonged and complex process that remains challenging for both computational methods and medicinal chemists and has not been able to be resolved using...

Contemporary research on machine learning (ML), especially the ones using natural language processing has unveiled a new paradigm of opportunities. The language like representation of molecules is...

The PairF-Net neural network potential approach is extended to incorporate energy conservation intrinsically and coupling to a molecular mechanical (MM) environment within the OpenMM package. The...

We present a graph-theory-based reformulation of all ONIOM-based molecular fragmentation methods. We discuss applications to (a) accurate post-Hartree–Fock AIMD that can be conducted at DFT cost for...

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most...

Scientific Reports - DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential...

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this system...

The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods. Metadynamics applies a...

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching...

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently,...

Nature Communications - Obtaining good initial structures is the main challenge for the computational study of transition states. Here, fast and accurate predictions for transition state of gas...

Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing...

Nature Machine Intelligence - Training a machine learning model with multiple tasks can create more-useful representations and achieve better performance than training models for each task...