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

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.

Abstract

Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31–70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol−1 (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.

Abstract

Machine learning (ML) methods enable computers to address problems by learning from existing data. Such applications are becoming commonplace in molecular sciences. Interest in applying ML techniques across chemical compound space, from predicting properties to designing molecules and materials is in the surge. Especially, ML models have started to accelerate computational chemistry, and are often as accurate as state‐of‐the‐art electronic/atomistic models. Being an integral part of the ML architecture, representation of a molecular entity, uniquely encoded, plays a crucial role to what extent an ML model would be accurately predicting the desired property. This review aims to demonstrate a hierarchy of representations which has been introduced, to capture all degrees of freedom of a molecule or an atom the best, to map the quantum mechanical properties. We discuss their diverse applications how …

Abstract

Facilitating the discovery of drugs by combining diverse compounds is becoming prevalent, especially for treating complex diseases like cancers and HIV. A drug is a chemi- cal compound structure and any sub-structure of a chemical compound is designated as a fragment. A chemical compound or a fragment can be modeled as a graph structure. Given a set of chemical compounds and their corresponding large set of fragments modeled as graph structures, we address the problem of identifying potential combinations of diverse chemical compounds, which cover a certain percentage of the set of fragments. In this regard, the key contributions of this work are three-fold: First, we introduce the notion of Graph Transactional Coverage Patterns (GTCPs) for any given graph transactional dataset. Second, we propose an efficient model and framework for extracting GTCPs from a given graph transactional dataset. Third, we conduct an extensive performance study using three real datasets to demonstrate that it is indeed feasible to efficiently extract GTCPs using our proposed GTCP-extraction framework. We also demonstrate the effectiveness of the GTCP-extraction framework through a case study in computer-aided drug design. Index Terms—Graph mining, Graph transactions, Coverage patterns, Drug discovery