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1. Identifying Systematic Force Field Errors Using a 3D-RISM Element Counting Correction

   https://doi.org/10.3390/molecules28030925  

   Casillas, Lizet; Grigorian, Vahe M.; Luchko, Tyler (February 2023, Molecules)

   Hydration free energies of small molecules are commonly used as benchmarks for solvation models. However, errors in predicting hydration free energies are partially due to the force fields used and not just the solvation model. To address this, we have used the 3D reference interaction site model (3D-RISM) of molecular solvation and existing benchmark explicit solvent calculations with a simple element count correction (ECC) to identify problems with the non-bond parameters in the general AMBER force field (GAFF). 3D-RISM was used to calculate hydration free energies of all 642 molecules in the FreeSolv database, and a partial molar volume correction (PMVC), ECC, and their combination (PMVECC) were applied to the results. The PMVECC produced a mean unsigned error of 1.01±0.04kcal/mol and root mean squared error of 1.44±0.07kcal/mol, better than the benchmark explicit solvent calculations from FreeSolv, and required less than 15 s of computing time per molecule on a single CPU core. Importantly, parameters for PMVECC showed systematic errors for molecules containing Cl, Br, I, and P. Applying ECC to the explicit solvent hydration free energies found the same systematic errors. The results strongly suggest that some small adjustments to the Lennard–Jones parameters for GAFF will lead to improved hydration free energy calculations for all solvent models. 

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2. Teaching a neural network to attach and detach electrons from molecules

   https://doi.org/10.1038/s41467-021-24904-0  

   Zubatyuk, Roman; Smith, Justin S.; Nebgen, Benjamin T.; Tretiak, Sergei; Isayev, Olexandr (December 2021, Nature Communications)

   Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions. 

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3. Binding free energy analysis of galectin‐3 natural ligands and synthetic inhibitors

   https://doi.org/10.1002/pro.70143  

   Newman, Luke; Vaissier_Welborn, Valerie (June 2025, Protein Science)

   Abstract Galectin‐3–ligand complexes are characterized by halogen, σ‐hole bonds, hydrogen bonds, cation‐π and CH‐π interactions. Here, we model these non‐covalent interactions with the AMOEBA polarizable force field and conduct an absolute binding free energy analysis on leading galectin‐3 inhibitors. Synthetic drug molecules GB0139, GB1107, and GB1211 were estimated to have binding free energies of −4.3, −6.7, and −9.5 kcal/mol respectively. This compares to −0.3 and 1.4 kcal/mol for the natural ligands, N‐acetyllactosamine type 1 and type 2, respectively. We calculated the electric fields projected along key bonds in each ligand to further rationalize these results. We find that while the hydroxyl groups of the natural ligands interact reasonably well with residues in galectin‐3's binding pocket, structural dynamics weaken the binding pose and favor interactions with water, sometimes yielding to dissociation. In contrast, the more favorable binding energy of GB1211, leading inhibitor in clinical studies, is associated with strong and constant electric fields across the bonds investigated, suggesting a stiffer binding pose with a stabilizing σ‐hole interaction. 

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4. Accurate Interaction Energies of CO ₂ with the 20 Naturally Occurring Amino Acids

   https://doi.org/10.1002/cphc.202300027  

   Sylvanus, Amarachi G.; Vogiatzis, Konstantinos D. (July 2023, ChemPhysChem)

   Abstract We have performed a series of highly accurate calculations between CO₂and the 20 naturally occurring amino acids for the investigation of the attractive noncovalent interactions. Different nucleophilic groups present in the amino acid structures were considered (α‐NH₂, COOH, side groups), and the stronger binding sites were identified. A database of accurate reference interactions energies was compiled as computed by explicitly‐correlated coupled‐cluster singles‐and‐doubles, together with perturbative triples extrapolated to the complete‐basis‐set limit. The CCSD(F12)(T)/CBS reference values were used for comparing a variety of popular density functionals with different basis sets. Our results show that most density functionals with the triple‐zeta basis set def2‐TZVPP align with the CCSD(F12)(T)/CBS reference values, but errors range from 0.1 kcal/mol up to 1.0 kcal/mol. 

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5. Quantum chemical accuracy from density functional approximations via machine learning

   https://doi.org/10.1038/s41467-020-19093-1  

   Bogojeski, Mihail; Vogt-Maranto, Leslie; Tuckerman, Mark E.; Müller, Klaus-Robert; Burke, Kieron (December 2020, Nature Communications)

   null (Ed.)

   Abstract Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol −1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol −1 ) on test data. Moreover, density-based Δ -learning (learning only the correction to a standard DFT calculation, termed Δ -DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ -DFT  is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C 6 H 4 (OH) 2 ) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ -DFT  facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. 

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