PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method.

Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction.

Heats of formation on the way from BH to BH: thermochemical consequences of multicenter bonding in and DFT methods.

The objective of this study is to evaluate the effectiveness of various computational methods in reproducing the experimental heats of formation of boron hydrides using the atomization energy approach. The results have demonstrated that the empirical dispersion combined with the BJ damping function provided too large intramolecular dispersion energies, thereby compromising the accuracy of the outcomes produced by the DFT-D3 methods. Additionally, the CCSD(T) method has reproduced the experimental values only when combined with a basis set optimized for an accurate description of the core-valence correlation effect.

Extensive targeting of chemical space at the prime side of ketoamide inhibitors of rhomboid proteases by branched substituents empowers their selectivity and potency.

Rhomboid intramembrane serine proteases have been implicated in several pathologies, and emerge as attractive pharmacological target candidates. The most potent and selective rhomboid inhibitors available to date are peptidyl α-ketoamides, but their selectivity for diverse rhomboid proteases and strategies to modulate it in relevant contexts are poorly understood. This gap, together with the lack of suitable in vitro models, hinders ketoamide development for relevant eukaryotic rhomboid enzymes.

Working with benchmark datasets in the Cuby framework.

The development and benchmarking of computational chemistry methods rely on comparison with benchmark data. More and larger benchmark datasets are becoming available, and working efficiently with them is a necessity. The Cuby framework provides rich functionality for working with datasets, comes with many ready-to-use predefined benchmark sets, and interfaces with a wide range of computational chemistry software packages.

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes.

Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods.

Improved Description of Intra- and Intermolecular Interactions through Dispersion-Corrected Second-Order Møller-Plesset Perturbation Theory.

ConspectusThe quantum chemical modeling of organic crystals and other molecular condensed-phase problems requires computationally affordable electronic structure methods which can simultaneously describe intramolecular conformational energies and intermolecular interactions accurately. To achieve this, we have developed a spin-component-scaled, dispersion-corrected second-order Møller-Plesset perturbation theory (SCS-MP2D) model. SCS-MP2D augments canonical MP2 with a dispersion correction which removes the uncoupled Hartree-Fock dispersion energy present in canonical MP2 and replaces it with a more reliable coupled Kohn-Sham treatment, all evaluated within the framework of Grimme's D3 dispersion model.

On the Role of Charge Transfer in Many-Body Non-Covalent Interactions.

Charge transfer is one of the mechanisms involved in non-covalent interactions. In molecular dimers, its contribution to pairwise interaction energies has been studied extensively using a variety of interaction energy decomposition schemes. In polar interactions such as hydrogen bonds, it can contribute ten or several tens of percent of the interaction energy.

Non-covalent interactions atlas benchmark data sets 4: σ-hole interactions.

The SH250×10 dataset presented here extends the Non-Covalent Interactions Atlas database (https://www.nciatlas.org) to complexes bound by σ-hole interactions - halogen, chalcogen and pnictogen bonds.

Non-Covalent Interactions Atlas benchmark data sets 5: London dispersion in an extended chemical space.

The Non-Covalent Interactions Atlas (https://www.nciatlas.org) has been extended with two data sets of benchmark interaction energies in complexes dominated by London dispersion.

Spin-component-scaled and dispersion-corrected second-order Møller-Plesset perturbation theory: a path toward chemical accuracy.

Second-order Møller-Plesset perturbation theory (MP2) provides a valuable alternative to density functional theory for modeling problems in organic and biological chemistry. However, MP2 suffers from known limitations in the description of van der Waals (London) dispersion interactions and reaction thermochemistry. Here, a spin-component-scaled, dispersion-corrected MP2 model (SCS-MP2D) is proposed that addresses these weaknesses.

Conformational energies and equilibria of cyclic dinucleotides in vacuo and in solution: computational chemistry vs. NMR experiments.

Performance of computational methods in modelling cyclic dinucleotides - an important and challenging class of compounds - has been evaluated by two different benchmarks: (1) gas-phase conformational energies and (2) qualitative agreement with NMR observations of the orientation of the χ-dihedral angle in solvent. In gas-phase benchmarks, where CCSD(T) and DLPNO-CCSD(T) methods have been used as the reference, most of the (dispersion corrected) density functional approximations are accurate enough to justify prioritizing computational cost and compatibility with other modelling options as the criterion of choice. NMR experiments of 3'3'-c-di-AMP, 3'3'-c-GAMP, and 3'3'-c-di-GMP show the overall prevalence of the anti-conformation of purine bases, but some population of syn-conformations is observed for guanines.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts.

The new R739×5 data set from the Non-Covalent Interactions Atlas series (www.nciatlas.org) focuses on repulsive contacts in molecular complexes, covering organic molecules, sulfur, phosphorus, halogens, and noble gases.

Benchmark Data Sets of Boron Cluster Dihydrogen Bonding for the Validation of Approximate Computational Methods.

The success of approximate computational methods, such as molecular mechanics, or dispersion-corrected density functional theory, in the description of non-covalent interactions relies on accurate parameterizations. Benchmark data sets are thus required. This area is well developed for organic molecules and biomolecules but practically non-existent for boron clusters, which have been gaining in importance in modern drug as well as material design.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design.

Invited for this month's cover is the group of Prof. Pavel Hobza, Czech Academy of Sciences, Prague. The cover picture shows a powerful automated quantum mechanics based SQM/COSMO approach to protein-ligand scoring.

Non-Covalent Interactions Atlas Benchmark Data Sets 2: Hydrogen Bonding in an Extended Chemical Space.

The Non-Covalent Interactions Atlas (www.nciatlas.org) aims to provide a new generation of benchmark data sets for noncovalent interactions.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design.

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e.

Non-Covalent Interactions Atlas Benchmark Data Sets: Hydrogen Bonding.

The Non-Covalent Interactions Atlas project (www.nciatlas.org) aims to cover a wide range of noncovalent interactions with a new generation of benchmark data sets.

Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design.

The semiempirical quantum mechanical (SQM) methods used in drug design are commonly parametrized and tested on data sets of systems that may not be representative models for drug-biomolecule interactions in terms of both size and chemical composition. This is addressed here with a new benchmark data set, PLF547, derived from protein-ligand complexes, consisting of complexes of ligands with protein fragments (such as amino-acid side chains), with interaction energies based on MP2-F12 and DLPNO-CCSD(T) calculations. From these, composite benchmark interaction energies are also built for complexes of the ligand with the complete active site of the protein (PLA15 data set).

Impressive Enrichment of Semiempirical Quantum Mechanics-Based Scoring Function: HSP90 Protein with 4541 Inhibitors and Decoys.

The image shows the ability of SQM-based frame to separate the actives (background: green spheres) from inactives (red spheres) while maintaining a powerful sampling (front: HSP90 crystal complex). Read the full text of the Article at 10.1002/cphc.

Impressive Enrichment of Semiempirical Quantum Mechanics-Based Scoring Function: HSP90 Protein with 4541 Inhibitors and Decoys.

This paper describes the excellent performance of a newly developed scoring function (SF), based on the semiempirical QM (SQM) PM6-D3H4X method combined with the conductor-like screening implicit solvent model (COSMO). The SQM/COSMO, Amber/GB and nine widely used SFs have been evaluated in terms of ranking power on the HSP90 protein with 72 biologically active compounds and 4469 structurally similar decoys. Among conventional SFs, the highest early and overall enrichment measured by EF and AUC% obtained using single-scoring-function ranking has been found for Glide SP and Gold-ASP SFs, respectively (7, 75 % and 3, 76 %).

Description of halogen bonding in semiempirical quantum-mechanical and self-consistent charge density-functional tight-binding methods.

This article analyzes the ability of semiempirical quantum-mechanical methods (PM6 and PM7) and self-consistent charge density-functional tight-binding (SCC-DFTB) method DFTB3 to describe halogen bonds. Calculations of the electrostatic potential on the surface of molecules containing halogens show that the σ-hole could be described well in modified neglect of diatomic overlap-based methods. The situation is more complex in the case of DFTB3 where a simpler model is used for the electrostatics, but short-ranged effects are covered in the Hamiltonian.

Reparametrization of the COSMO Solvent Model for Semiempirical Methods PM6 and PM7.

An accurate description of solvation effects is of high importance in modeling biomolecular systems. Our main interest is to find an accurate yet efficient solvation model for semiempirical quantum-mechanical methods applicable to large protein-ligand complexes in the context of computer-aided drug design. We present a survey of readily available methods and a new reparametrization of the COSMO solvent model for PM6 and PM7 calculations in MOPAC.

Gating the electron transfer at a monocopper centre through the supramolecular coordination of water molecules within a protein chamber mimic.

Functionality of enzymes is strongly related to water dynamic processes. The control of the redox potential for metallo-enzymes is intimately linked to the mediation of water molecules in the first and second coordination spheres. Here, we report a unique example of supramolecular control of the redox properties of a biomimetic monocopper complex by water molecules.

Accurate Noncovalent Interactions via Dispersion-Corrected Second-Order Møller-Plesset Perturbation Theory.

Noncovalent interactions govern many important areas of chemistry, ranging from biomolecules to molecular crystals. Here, an accurate and computationally inexpensive dispersion-corrected second-order Møller-Plesset perturbation theory model (MP2D) is presented. MP2D recasts the highly successful dispersion-corrected MP2C model in a framework based on Grimme's D3 dispersion correction, combining Grimme's D3 dispersion coefficients with new analogous uncoupled Hartree-Fock ones and five global empirical parameters.

Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking.

General and reliable description of structures and energetics in protein-ligand (PL) binding using the docking/scoring methodology has until now been elusive. We address this urgent deficiency of scoring functions (SFs) by the systematic development of corrected semiempirical quantum mechanical (SQM) methods, which correctly describe all types of noncovalent interactions and are fast enough to treat systems of thousands of atoms. Two most accurate SQM methods, PM6-D3H4X and SCC-DFTB3-D3H4X, are coupled with the conductor-like screening model (COSMO) implicit solvation model in so-called "SQM/COSMO" SFs and have shown unique recognition of native ligand poses in cognate docking in four challenging PL systems, including metalloprotein.