Abstract

Spectroscopy is the study of how matter interacts with electromagnetic radiation. The spectra of any molecule are highly information-rich, yet the inverse relation of spectra to the corresponding molecular structure is still an unsolved problem. Nuclear magnetic resonance (NMR) spectroscopy is one such critical technique in the scientists’ toolkit to characterize molecules. In this work, a novel machine learning framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure given an NMR spectra. The proposed framework uses a combination of online Monte Carlo tree search (MCTS) and a set of graph convolution networks to build a molecule iteratively. Our method can predict the structure of the molecule ∼80% of the time in its top 3 guesses for molecules with <10 heavy atoms. We believe that the proposed framework is a significant step in solving the

Abstract

Ketones are the key functional group that recurs in chemistry and biology, and accessing them through simple and economic ways is highly desirable. Herein, we report the synthesis of unsymmetrical ketones from abundant toluene and alkyl esters, where volatile alcohols are the sole byproduct. This protocol applies to a repertoire of substrates bearing electron‐donating, electron‐withdrawing, and neutral substituents. Most importantly, the organometallic ferrocenyl ester underwent aroylation with ease. This method is the first example to furnish diketones from methyl arenes and diesters. Furthermore, cyclic imide was synthesized by this protocol utilizing KN(SiMe3)2 as a ′nitrogen′ source. Density functional theory studies provide insight into deprotonation of toluene by K+‐π interaction by increasing its acidity, and this being the rate‐determining step.

Abstract

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding afnity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding afnities better than the state-of-the-art scoring functions are important. For the frst time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding afnities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding afnities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding afnities

Abstract

A unique B–N coordinated phenanthroimidazole-based zinc salen was synthesized. The zinc salen thus synthesized acts as a photocatalyst for the cycloaddition of carbon dioxide with terminal epoxides under ambient conditions. DFT study of the cycloaddition of carbon dioxide with terminal epoxide indicates the preference of the reaction pathway when photocatalyzed by zinc salen. We anticipate that this strategy will help to design new photocatalysts for CO2 fixation.

Abstract

Nucleocytoplasmic shuttling of viral elements, supported by several host factors, is essential for the replication of the human immunodeficiency virus (HIV). HIV‐1 uses a nuclear RNA export pathway mediated by viral protein Rev to transport its Rev response element (RRE)‐containing partially spliced and unspliced transcripts aided by the host nuclear RNA export protein CRM1. The factor(s) interacting with the CRM1‐Rev complex are potential antiretroviral target(s) and could serve as a retroviral model system to study nuclear export machinery adapted by these viruses. We earlier reported that cellular Staufen‐2 interacts with Rev, facilitating viral‐RNA export. Here, we identified the formation of a complex between Staufen‐2, CRM1 and Rev. Molecular docking and simulations mapped the interacting residues in the RNA‐binding Domain 4 of Staufen‐2 as R336 and R337, which were experimentally verified to be …

Abstract

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the mos

Abstract

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has 16 highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug 17 discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and 18 sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this 19 scenario, application of efficient state-of-the-art computational methods and modern artificial 20 intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs 21 and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a 22 powerful option to save resources and time. Structure-based drug repurposing is a popular in silico 23 repurposing approach. In this review, we discuss traditional and modern AI-based computational 24 methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. 25 Additionally, we highlight the role of generative m

Abstract

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

Abstract

Here we report the synthesis of an N-heterocyclic carbene (NHC)-stabilised phosphinidene oxide by the controlled oxygenation of a phosphinidene under ambient conditions. This compound can be further oxygenated to a phosphinidene dioxide. The stoichiometric reduction of a phosphinidene oxide with KC8 resembles the pinacol coupling reaction–the reduction of a carbonyl compound. We also looked at the stoichiometric oxidation of NHC-coordinated phosphinidene, phosphinidene oxide and phosphinidene dioxide with [NO][SbF6].

Abstract

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue’s contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.