Pharmacophore models are widely used in computational drug discovery (e.g., in the virtual screening of drug molecules) to capture essential information about interactions between ligands and a target protein. Generating pharmacophore models from protein structures is typically a manual process, but there has been growing interest in automated pharmacophore generation methods. Automation makes feasible

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking datasets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool

Reliable information on partition coefficients plays a key role in drug development, as solubility decisively affects bioavailability. In a physico-chemical context the partition coefficient of a solute between two different solvents can be described as a function of solvation free energies. Hence, substantial scientific efforts are dedicated towards accurate predictions of solvation free energies

Accurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with high Topt values are desired for enhanced reaction rates. Recently, a machine-learning method (TOME) for predicting Topt was developed. TOME was trained on a normally-distributed dataset with a median Topt of 37°C and less than five percent of Topt values above 85°C, limiting the method's

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more

Falcipain-2 (FP-2) is hemoglobinase considered an attractive drug target of Plasmodium falciparum. Recently, it has been shown that peptidomimetic nitriles containing a 3-pyridine (3Pyr) moiety at P2 display high affinity and selectivity for FP-2 with respect to human cysteine cathepsins (hCats). Further characterization demonstrated that certain P3 variants of these compounds possess micromolar inhibition

Glycans play a vital role in a large number of cellular processes. Their complex and flexible nature hampers structure-function studies using experimental techniques. Molecular dynamics (MD) simulations can help in understanding dynamic aspects of glycans if the forcefield parameters used can reproduce key experimentally observed properties. Here, we present optimized coarse-grained (CG) Martini forcefield

Small molecule compounds which form colloidal aggregates in solution are problematic in early drug discovery: adsorption of the target protein by these aggregates can lead to false positives in inhibition assays. In this work, we probe the molecular basis of this inhibitory mechanism using molecular dynamics simulations. Specifically, we examine in aqueous solution the adsorption of the enzymes β-lactamase

Development of new potent, safe drugs to treat Mycobacteria has proven to be challenging, with limited hit rates of initial screens restricting subsequent development efforts. Despite significant efforts and evolution of Quantitative Structure-Activity Relationship (QSAR) as well as machine learning-based models for computationally predicting molecule bioactivity, there is an unmet need for efficient

We present the graph-based molecule software Molassembler for building organic and inorganic molecules. Molassembler provides algorithms for the construction of molecules built from any set of elements from the periodic table. In particular, poly-nuclear transition metal complexes and clusters can be considered. Structural information is encoded as a graph. Stereocenter configurations are interpretable

Accurate prediction of NMR chemical shifts at affordable computational cost is very important for different types of structural assignments in experimental studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) are two of the most popular computational methods for NMR calculation, yet, they often fail to resolve ambiguities in structural assignments. Here, we present a new

OOver the past few decades, virtual high-throughput screening (vHTS) and molecular dynamics (MD) simulations have become effective and widely used tools in the initial stages of drug discovery efforts. These methods allow a great number of druglike molecules to be screened quickly and inexpensively. Unfortunately, however, the accuracies of both these methods rely on the quality of the underlying Molecular

We assess the performance of molecular density functional theory (MDFT) to predict hydration free energies of the small drug-like molecules benchmark, FreeSolv. MDFT in the hyper-netted chain approximation coupled with a pressure correction predicts experimental hydration free energies of the FreeSolv database within 1 kcal/mol with an average computation time of two cpu.min per molecule. This is the

Accurate conformations of a molecule are critical for reliable prediction of its properties, so good predictive models require good conformations. Here we present a method for conformer sampling based on distance geometry, implemented in our conformation generator OMEGA, which we apply to both macrocycles and drug-like molecules. We validate it in the usual fashion, reproducing conformations from the

This paper presents TorchANI, a PyTorch based software for training/inference of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical properties of molecular systems. ANI is an accurate neural network potential originally implemented using C++/CUDA in a program called NeuroChem. Compared with NeuroChem, TorchANI has a design emphasis on being light weight, user

The following joint Editorial was originally published in the Journal of the American Chemical Society (DOI: 10.1021/jacs.0c04253). The research community faces unprecedented disruption from the global COVID-19 pandemic. Some researchers are beginning to return to lab work after a hiatus, but others continue to work as best as they can from home, including ourselves. The global editorial, support,