Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms

Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and machine learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from density functional theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire.

In silico screening of the phytochemicals present in Clitoria ternatea L. as the inhibitors of snake venom phospholipase A2 (PLA2)

Millions of people suffer from snake bite envenomation, and its management is a challenge, even today. Medicinal plants have attracted the researcher's attention for their outstanding advantages in treating many diseases, including snake venom poisoning. Clitoria ternatea L, is a plant popularly known for its various pharmacological effects especially, anti-snake venom property. However, the molecular mechanism behind this is poorly understood. It is reported that snake venom PLA2 is an extensively studied toxic factor. This study is meant to screen the compound's capability to act as inhibitors of the Daboia russelli snake venom PLA2 through molecular docking and dynamics studies. Our results show that among the 27 compounds taken for the study, only Kaempferol showed good interaction profile with the conserved catalytic active site residues, His48 and Asp49. The pharmacophore features of the compound also demonstrate its exact fitting at the binding pocket. Further RMSD, RMSF, Rg, and hydrogen bond analysis confirmed the stable binding of Kaempferol with PLA2 through molecular dynamic simulations for 100 ns. In addition, the MM/PBSA binding free energy calculation of the complex was also affirming the docking results. The binding free energy (BFE) of Kaempferolis better than the reference compound. ADME and Lipinski's rule of five reveals its drug like properties.Communicated by Ramaswamy H. Sarma.

The pKBHX Hydrogen-Bond Basicity Scale: From Molecules to Anions

The pK_BHX (logarithm of complexation constant K of 4-fluorophenol with bases) hydrogen-bond basicity scale of neutral hydrogen-bond acceptors (HBAs) is extended to anionic HBAs. The scale is constructed for 26 anions through (i) the infrared measurement of K on NBu₄⁺X^- ion pairs in CCl₄, (ii) the estimation of K from linear free energy relationships between measured K values and literature K values for various phenols in polar solvents, and (iii) the computation of K at the density functional theory level in CCl₄. The scale extends on a 9.4 pK unit range from fluoride to tetraphenylborate. Considering a number of anions as organic functions substituted with unipolar substituents, their pK_BHX values can be related to the Hammett-Taft substituent constants σ. Unipolar substituents (O^- and S^-) obey the same pK_BHX versus σ relationships as dipolar ionic (N^-N⁺R₃) and dipolar (OH, CF₃, NR₂, or OR) ones for the nitrile, carbonyl, nitroso, nitro, sulfonyl, and phosphoryl functions. Like dipolar substituents, unipolar substituents at carbon and nitrogen operate by field-inductive and resonance effects, whereas substituents at sulfur and phosphorus operate only by the field-inductive effect.

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.

Bayer's in silico ADMET platform: a journey of machine learning over the past two decades

Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.

The art of atom descriptor design

This review provides an overview of descriptions of atoms applied to the understanding of phenomena like chemical reactivity and selectivity, pK_a values, Site of Metabolism prediction, or hydrogen bond strengths, but also the substitution of quantum mechanical calculations by machine learning models for energies, forces or even spectrosocopic properties and finally the fast calculation of atomic charges for force field parametrization. The descriptor space ranges from derivatives of the wavefunctions or electron density via quantum mechanics derived descriptors to classical descriptions of atoms and their embedding in a molecule. The common denominator for all approaches is the thorough understanding of the physics of the chemical problem that guided the design of the atom descriptor. Quantum mechanics (QM) and machine learning (ML) finally are converging to a new discipline, namely QM/ML.

Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol-1 (acceptors), and 2.3 kJ mol-1 (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding.