Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods.

Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated.

Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression.

We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure.

Solution-Phase Conformational/Vibrational Anharmonicity in Comonomer Incorporation Polyolefin Catalysis.

The prediction of comonomer incorporation statistics in polyolefin catalysis necessitates an accurate calculation of free energies corresponding to monomer binding and insertion, often requiring sub-kcal/mol resolution to resolve experimental free energies. Batch reactor experiments are used to probe incorporation statistics of ethene and larger α-olefins for three constrained geometry complexes which are employed as model systems. Herein, over 6 ns of quantum mechanics/molecular mechanics (QM/MM) molecular dynamics is performed in combination with the zero-temperature string method to characterize the solution-phase insertion barrier and to analyze the contributions from conformational and vibrational anharmonicity arising both in vacuum and in solution.

Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning.

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A MOB pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of MOs. The dipole MOB features consist of the energy MOB features and their responses to electric fields.

Informing geometric deep learning with electronic interactions to accelerate quantum chemistry.

Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning.

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space.

We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [ 2019, 15, 6668] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantages of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and exhibiting improved performance with an increasing number of training examples.

Fast Near Potential Energy Surfaces Using Machine Learning.

A machine-learning based approach for evaluating potential energies for quantum mechanical studies of properties of the ground and excited vibrational states of small molecules is developed. This approach uses the molecular-orbital-based machine learning (MOB-ML) method to generate electronic energies with the accuracy of CCSD(T) calculations at the same cost as a Hartree-Fock calculation. To further reduce the computational cost of the potential energy evaluations without sacrificing the CCSD(T) level accuracy, GPU-accelerated Neural Network Potential Energy Surfaces (NN-PES) are trained to geometries and energies that are collected from small-scale Diffusion Monte Carlo (DMC) simulations, which are run using energies evaluated using the MOB-ML model.

Using real-world data in pediatric clinical trials: Lessons learned and future applications in studies of persistent pulmonary hypertension of the newborn.

Persistent pulmonary hypertension of the newborn (PPHN) is a complication of term birth, characterized by persistent hypoxemia secondary to failure of normal postnatal reduction in pulmonary vascular resistance, with potential for short- and long-term morbidity and mortality. The primary pharmacologic goal for this condition is reduction of the neonate's elevated pulmonary vascular resistance with inhaled nitric oxide, the only approved treatment option. Various adjunctive, unapproved therapeutics have been trialed with mixed results, likely related to challenges with recruiting the full, intended patient population into clinical studies.

Equilibrium-nonequilibrium ring-polymer molecular dynamics for nonlinear spectroscopy.

Two-dimensional Raman and hybrid terahertz-Raman spectroscopic techniques provide invaluable insight into molecular structures and dynamics of condensed-phase systems. However, corroborating experimental results with theory is difficult due to the high computational cost of incorporating quantum-mechanical effects in the simulations. Here, we present the equilibrium-nonequilibrium ring-polymer molecular dynamics (RPMD), a practical computational method that can account for nuclear quantum effects on the two-time response function of nonlinear optical spectroscopy.

Coordination of -1 programmed ribosomal frameshifting by transcript and nascent chain features revealed by deep mutational scanning.

Programmed ribosomal frameshifting (PRF) is a translational recoding mechanism that enables the synthesis of multiple polypeptides from a single transcript. During translation of the alphavirus structural polyprotein, the efficiency of -1PRF is coordinated by a 'slippery' sequence in the transcript, an adjacent RNA stem-loop, and a conformational transition in the nascent polypeptide chain. To characterize each of these effectors, we measured the effects of 4530 mutations on -1PRF by deep mutational scanning.

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy.

We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.

Electronic Structure of Superoxidized Radical Cationic Dodecaborate-Based Clusters.

The expanding field of boron clusters has attracted continuous theoretical efforts to understand their diverse structures and unique bonding. We recently discovered a new reversible redox event of B(-3-methylbutyl) in which the superoxidized radical cationic form [B(-3-methylbutyl)] was identified and isolated for the first time. Herein, comprehensive (TD-)DFT studies in tandem with electrochemical experiments were employed to demonstrate the generality of the reported behavior across perfunctionalized B(OR) clusters (R = aryl or alkyl).

A New Imaginary Term in the Second-Order Nonlinear Susceptibility from Charged Interfaces.

Nonresonant second harmonic generation (SHG) phase and amplitude measurements obtained from the silica-water interface at varying pH values and an ionic strength of 0.5 M point to the existence of a nonlinear susceptibility term, which we call χ, that is associated with a 90° phase shift. Including this contribution in a model for the total effective second-order nonlinear susceptibility produces reasonable point estimates for interfacial potentials and second-order nonlinear susceptibilities when χ ≈ 1.

Force transduction creates long-ranged coupling in ribosomes stalled by arrest peptides.

Force-sensitive arrest peptides regulate protein biosynthesis by stalling the ribosome as they are translated. Synthesis can be resumed when the nascent arrest peptide experiences a pulling force of sufficient magnitude to break the stall. Efficient stalling is dependent on the specific identity of a large number of amino acids, including amino acids that are tens of angstroms away from the peptidyl transferase center (PTC).

Efficient Copolymerization of Acrylate and Ethylene with Neutral P, O-Chelated Nickel Catalysts: Mechanistic Investigations of Monomer Insertion and Chelate Formation.

The efficient copolymerization of acrylates with ethylene using Ni catalysts remains a challenge. Herein, we report two neutral Ni(II) catalysts (POP-Ni-py () and PONap-Ni-py ()) that exhibit high thermal stability and significantly higher incorporation of polar monomer (for ) or improved resistance to -butylacrylate (tBA)-induced chain transfer (for ), in comparison to previously reported catalysts. Nickel alkyl complexes generated after tBA insertion, POP-Ni-CCO(py) () and PONap-Ni-CCO(py) (), were isolated and, for the first time, characterized by crystallography.

Analytical gradients for molecular-orbital-based machine learning.

Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.

Small Nuclear Quantum Effects in Scattering of H and D from Graphene.

We study nuclear quantum effects in H/D sticking to graphene, comparing scattering experiments at near-zero coverage with classical, quantized, and transition-state calculations. The experiment shows H/D sticking probabilities that are indistinguishable from one another and markedly smaller than those expected from a consideration of zero-point energy shifts of the chemisorption transition state. Inclusion of dynamical effects and vibrational anharmonicity via ring-polymer molecular dynamics (RPMD) yields results that are in good agreement with the experimental results.

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states.

Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet's theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model.

A generalized class of strongly stable and dimension-free T-RPMD integrators.

Recent work shows that strong stability and dimensionality freedom are essential for robust numerical integration of thermostatted ring-polymer molecular dynamics (T-RPMD) and path-integral molecular dynamics, without which standard integrators exhibit non-ergodicity and other pathologies [R. Korol et al., J.

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features.

We introduce a machine learning method in which energy solutions from the Schrödinger equation are predicted using symmetry adapted atomic orbital features and a graph neural-network architecture. OrbNet is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison [Int.

Sum-Frequency Signals in 2D-Terahertz-Terahertz-Raman Spectroscopy.

We demonstrate that halogenated methane (HM) two-dimensional (2D)-terahertz-terahertz-Raman (2D-TTR) spectra are determined by the complicated structure of the instrument response function (IRF) along ω and by the molecular coherences along ω. Experimental improvements have helped increase the resolution and dynamic range of the measurements, including accurate THz pulse shape characterization. Sum-frequency excitations convolved with the IRF are found to quantitatively reproduce the 2D-TTR signal.

A Super-Oxidized Radical Cationic Icosahedral Boron Cluster.

While the icosahedral -[BH] cluster does not display reversible electrochemical behavior, perfunctionalization of this species via substitution of all 12 B-H vertices with alkoxy or benzyloxy (OR) substituents engenders reversible redox chemistry, providing access to clusters in the dianionic, monoanionic, and neutral forms. Here, we evaluated the electrochemical behavior of the electron-rich B(O-3-methylbutyl) () cluster and discovered that a new reversible redox event that gives rise to a fourth electronic state is accessible through one-electron oxidation of the neutral species. Chemical oxidation of with [N(2,4-BrCH)] afforded the isolable [] cluster, which is the first example of an open-shell cationic B cluster in which the unpaired electron is proposed to be delocalized throughout the boron cluster core.

Embedded Mean-Field Theory for Solution-Phase Transition-Metal Polyolefin Catalysis.

Decreasing the wall-clock time of quantum mechanics/molecular mechanics (QM/MM) calculations without sacrificing accuracy is a crucial prerequisite for widespread simulation of solution-phase dynamical processes. In this work, we demonstrate the use of embedded mean-field theory (EMFT) as the QM engine in QM/MM molecular dynamics (MD) simulations to examine polyolefin catalysts in solution. We show that employing EMFT in this mode preserves the accuracy of hybrid-functional DFT in the QM region, while providing up to 20-fold reductions in the cost per SCF cycle, thereby increasing the accessible simulation time-scales.