Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge

Leveraging DFT and Molecular Fragmentation for Chemically Accurate pKa Prediction Using Machine Learning

We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the pKas of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT. The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis). We believe the carefully curated input of physics-based features lessens the model's data dependence and need for complex deep learning architectures, without compromising the accuracy of the test sets. As a point of novelty, our work extends the applicability of CBH, employing it for the generation of viable molecular descriptors for ML.

Theoretical Study on the Alkylimino-Substituted Sulfonamides with Potential Biological Activity

Antibiotics play a key role in the fight against bacterial diseases. However, bacteria quickly learn how to minimize the effects of antibiotics and strengthen their resistance. Thus, the fight against them becomes more and more difficult and there is a constant search for new bactericidal compounds. It is important in this type of search to determine the basic properties of compounds such as pKa, hydrogen bond formation, or hydrophobicity. Here, we present the results of our in silico study of five sulfonamide derivatives differing in alkylamine substituent length. Based on our results, we propose a model of three possible pKa values for each of the studied compounds. Interestingly, the use of Muckerman's approach for pKa determination exhibits that theoretical and experimental results are in very good agreement. Intramolecular hydrogen bond formation affects pKa. The strength of the H-bond interaction increases from ethyl to butylamine and then decreases with the elongation of the alkylamine chain. The obtained partition coefficients (expressed here in the value of log P) increase with the number of carbon atoms in the alkylamine chain following Lipinski's rule of five. The presented results provide important structural, physicochemical, and thermodynamic information that allows for the understanding of the influence of some sulfonamides and their possible activity.

Protonation of Strained Epoxy Resin under Wet Conditions via First-Principles Calculations Using the H+-Shift Method

A significant challenge in adhesive bonding is the accelerated breaking of stretched adhesives under wet conditions, which is known as cohesive failure. One group of commonly used adhesives consists of the amine-cured epoxy resins. Based on deprotonation free-energy calculations of the unstrained resin in water, it has recently been proposed that these adhesives can undergo failure through breakage originating at the protonated amine group under neutral or acidic conditions [J. Phys. Chem. B 2021, 125, 8989-8996]. In this study, we comprehensively investigated the degree of protonation of the amine group under both stretched and compressed conditions by devising a robust first-principles protonation calculation method applicable to strained materials. It was found that the amine group was partially protonated in neutral water at 298 K and that the amine group was protonated when the epoxy resin was stretched to a greater extent in water, and vice versa. These findings support the physicochemical cause of cohesive failure due to protonation of the amine group in the stretched amine-cured epoxy resins.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Steric Control in Activator-Induced Nucleophilic Quencher Detachment-Based Probes: High-Contrast Imaging of Aldehyde Dehydrogenase 1A1 in Cancer Stem Cells

Turn-on fluorescence probes can visualize the enzyme activity with high contrast. We have established a new turn-on mechanism, activator-induced nucleophilic quencher detachment (AiQd), and developed AiQd-based turn-on fluorescence probes for the detection of enzymes. Herein, we demonstrate that the precise steric control efficiently quenches the fluorescence of AiQd-based turn-on probes before the enzymatic transformation. Theoretical calculation appropriately predicted the ratio of the fluorescence-quenched closed-ring form of probes. βC5S-A, which has a sterically demanding methyl group at the β-position of a fluorescence-quenching nucleophilic mercapto group, showed a low background signal. βC5S-A responded to aldehyde dehydrogenase 1A1 (ALDH1A1) with high selectivity, thereby enabling high-contrast live imaging of cancer stem cells (signal-to-noise ratio >10). The ALDH1A1-responsiveness of βC5S-A was not significantly affected by amino acids and biological thiols, such as cysteine and glutathione.

Machine learning methods for pKa prediction of small molecules: Advances and challenges

The acid-base dissociation constant (pK_a) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pK_a prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pK_a prediction, including descriptor-based and graph-based approaches, and summarize their pros and cons. Moreover, we highlight the current challenges and future directions regarding experimental data, crucial factors influencing pK_a and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.

Design and Synthesis of New α-hydroxy β-fluoro/β-trifluoromethyl and Unsaturated Phosphonates from Carbohydrate-Derived Building Blocks via Pudovik and Horner–Wadsworth–Emmons Reactions

Herein, we present the application of fluorinated carbohydrate-derived building blocks for α-hydroxy β-fluoro/β-trifluoromethyl and unsaturated phosphonates synthesis. Pudovik and Horner–Wadsworth–Emmons reactions were applied to achieve this goal. The proposed pathway of the key reactions is supported by the experimental results, as well as quantum chemical calculations. The structure of the products was established by spectroscopic (1D, 2D NMR) and spectrometric (MS) techniques. Based on our data received, we claim that the progress of the Pudovik and HWE reactions is significantly influenced by the acidic protons present in the molecules as assessed by pKa values of the reagent.

Improving Small Molecule pKa Prediction Using Transfer Learning With Graph Neural Networks

Enumerating protonation states and calculating microstate pK_a values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated microstate pK_a predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pK_a values significantly improving its performance on two challenging test sets. Combining the graph neural network model with Dimorphite-DL, an open-source program for enumerating ionization states, we have developed the open-source Python package pkasolver, which is able to generate and enumerate protonation states and calculate pK_a values with high accuracy.

MolGpka: A Web Server for Small Molecule pKa Prediction Using a Graph-Convolutional Neural Network

pK_a is an important property in the lead optimization process since the charge state of a molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane permeability, metabolism, and toxicity. Accurate and fast estimation of small molecule pK_a is vital during the drug discovery process. We present MolGpKa, a web server for pK_a prediction using a graph-convolutional neural network model. The model works by learning pK_a related chemical patterns automatically and building reliable predictors with learned features. ACD/pK_a data for 1.6 million compounds from the ChEMBL database was used for model training. We found that the performance of the model is better than machine learning models built with human-engineered fingerprints. Detailed analysis shows that the substitution effect on pK_a is well learned by the model. MolGpKa is a handy tool for the rapid estimation of pK_a during the ligand design process. The MolGpKa server is freely available to researchers and can be accessed at https://xundrug.cn/molgpka.

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK_a for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pK_a challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pK_a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK_a values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pK_a prediction methods.

An entry to 2-(cyclobut-1-en-1-yl)-1H-indoles through a cyclobutenylation/deprotection cascade

A transition-metal-free strategy for the synthesis of 2-(cyclobut-1-en-1-yl)-1H-indoles under mild conditions is described herein. A series of substituted 2-(cyclobut-1-en-1-yl)-1H-indoles are accessed by a one-pot cyclobutenylation/deprotection cascade from N-Boc protected indoles. Preliminary experimental and density functional theory calculations suggest that a Boc-group transfer is involved in the underlying mechanism.

Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Accurate and rapid prediction of pKa of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach

Data-augmented high-throughput QM approach to compute pK_a of transition metal hydride complexes with hDFT accuracy and low cost.

Predicting pKa Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods

The acid dissociation constant (pK_a) has an important influence on molecular properties crucial to compound development in synthesis, formulation, and optimization of absorption, distribution, metabolism, and excretion properties. We will present a method that combines quantum mechanical calculations, at a semi-empirical level of theory, with machine learning to accurately predict pK_a for a diverse range of mono- and polyprotic compounds. The resulting model has been tested on two external data sets, one specifically used to test pK_a prediction methods (SAMPL6) and the second covering known drugs containing basic functionalities. Both sets were predicted with excellent accuracy (root-mean-square errors of 0.7-1.0 log units), comparable to other methodologies using a much higher level of theory and computational cost.

SAMPL6 logP challenge: machine learning and quantum mechanical approaches

Two different types of approaches: (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.

Standard state free energies, not pKas, are ideal for describing small molecule protonation and tautomeric states

The pK_a is the standard measure used to describe the aqueous proton affinity of a compound, indicating the proton concentration (pH) at which two protonation states (e.g. A^- and AH) have equal free energy. However, compounds can have additional protonation states (e.g. AH₂⁺), and may assume multiple tautomeric forms, with the protons in different positions (microstates). Macroscopic pK_as give the pH where the molecule changes its total number of protons, while microscopic pK_as identify the tautomeric states involved. As tautomers have the same number of protons, the free energy difference between them and their relative probability is pH independent so there is no pK_a connecting them. The question arises: What is the best way to describe protonation equilibria of a complex molecule in any pH range? Knowing the number of protons and the relative free energy of all microstates at a single pH, ∆G°, provides all the information needed to determine the free energy, and thus the probability of each microstate at each pH. Microstate probabilities as a function of pH generate titration curves that highlight the low energy, observable microstates, which can then be compared with experiment. A network description connecting microstates as nodes makes it straightforward to test thermodynamic consistency of microstate free energies. The utility of this analysis is illustrated by a description of one molecule from the SAMPL6 Blind pK_a Prediction Challenge. Analysis of microstate ∆G°s also makes a more compact way to archive and compare the pH dependent behavior of compounds with multiple protonatable sites.

Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge

Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water-octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water-octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.