Abstract

Images in the medical domain are fundamentally different from the general domain images. Consequently, it is infeasible to directly employ general domain Visual Question Answering (VQA) models for the medical domain. Additionally, medical image annotation is a costly and time-consuming process. To overcome these limitations, we propose a solution inspired by self-supervised pretraining of Transformer-style architectures for NLP, Vision, and Language tasks. Our method involves learning richer medical image and text semantic representations using Masked Vision-Language Modeling as the pretext task on a large medical image+caption dataset. The proposed solution achieves new state-of-the-art performance on two VQA datasets for radiology images – VQA-Med 2019 and VQA-RAD, outperforming even the ensemble models of previous best solutions. Moreover, our solution provides attention maps which help in model interpretability.

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

A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from 1st January, 2018 till 28th February, 2020 for ligand binding site (LBS) detection. DeepPocket’s results on various binding site datasets and SC6K highlights its better

Abstract

Protein-drug interactions play important roles in many biological processes and therapeutics. Prediction of the active binding site of a protein helps discover and optimise these interactions leading to the design of better ligand molecules. The tertiary structure of a protein determines the binding sites available to the drug molecule. A quick and accurate prediction of the binding site from sequence alone without utilising the three-dimensional structure is challenging. Deep Learning has been used in a variety of biochemical tasks and has been hugely successful. In this paper, a Residual Neural Network (leveraging skip connections) is implemented to predict a protein's most active binding site. An Annotated Database of Druggable Binding Sites from the Protein DataBank, sc-PDB, is used for training the network. Features extracted from the Multiple Sequence Alignments (MSAs) of the protein generated using DeepMSA, such as Position-Specic Scoring Matrix (PSSM), Secondary Structure (SS3), and Relative Solvent Accessibility (RSA), are provided as input to the network. A weighted binary cross-entropy loss function is used to counter the substantial imbalance in the

Abstract

Synthetic ion channels are a promising technology in the medical and materials sciences because of their ability to conduct ions. Channels based on cyclodextrin, a cyclic oligomer of glucose, are of particular interest because of their non-toxicity and bio-compatibility. Using molecular dynamics-based free energy calculations, this study identifies cyclodextrin channel types that are best suited to serve as synthetic ion channels. Free energy profiles show that the connectivity in the channel determines whether the channel is cation-selective or anion-selective. Furthermore, the energy barrier for ion transport is governed by the number of glucose molecules constituting the cyclodextrin units of the channel. A detailed mechanism is proposed for ion transport through these channels. Findings from this study will aid in designing cyclodextrinbased channels that could be either cation-selective or anion-selective, by modifying the linkages of the channel or the number of glucose molecules in the cyclodextrin rings.

Abstract

Accuracy on Minnesota Solvation Database (MNSOL) There are classical and quantum-mechanical simulation studies that use the MNSOL as the reference database. We choose solvation model based on density(SMD) for comparison with our CIGIN Model. We also compared CIGIN Model with semi-empirical methods, pure COSMO, and COSMO-RS.

Abstract

Energetically unfavorable Watson–Crick (WC)-like tautomeric forms of nucleobases are known to introduce spontaneous mutations, and contribute to replication, transcription, and translation errors. Recent NMR relaxation dispersion techniques were able to show that wobble (w) G•U mispair exists in equilibrium with the short-lived, low-population WC-like enolic tautomers. Presently, we have investigated the wG•U → WC-like enolic reaction pathway using various theoretical methods: quantum mechanics (QM), molecular dynamics (MD), and combined quantum mechanics/molecular mechanics (QM/MM). The previous studies on QM gas phase calculations were inconsistent with experimental data. We have also explored the environmental effects on the reaction energies by adding explicit water. While the QM-profile clearly becomes endoergic in the presence of water, the QM/MM-profile remains consistently endoergic in the presence and absence of water. Hence, by including microsolvation and QM/MM calculations, the experimental data can be explained. For the G•Uenol → Genol•U pathway, the latter appears to be energetically more favorable throughout all computational models. This study can be considered as a benchmark of various computational models of wG•U to WC-like tautomerization pathways with and without the environmental effects, and may contribute on further studies of other mispairs as well.

Abstract

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner

Abstract

Aqueous urea stabilizes the unfolded states of protein due to their ability to solvate both hydrophilic and hydrophobic residues favorably. The nature of interactions that stabilize different types of amino acid side chains in their solvent exposed state is still not understood. To gain insights into the molecular level details of urea interactions with proteins in their unfolded states, we have performed atomistic molecular dynamics simulations and free energy calculations using the thermodynamic integration method on model systems representing side chains of all amino acids in different solvent environments (water and varying concentrations of aqueous urea). A systematic analysis of structural, energetic and dynamic parameters has been done to understand the detailed atomistic mechanism. The main aim of the current study is to unravel the nature of urea-amino acid interactions by emphasizing on the chemical nature …

Abstract

The transport of water through aquaporins is a dynamic process that involves rapid movement of a chain of water molecules through the pore of the aquaporin. Structures of aquaporins solved using X-ray crystallography have provided some insights into how water is transported through these channels, and how certain structural features of the pore might help exclude other solutes from passing through the pore. However, such techniques provide only a static picture, and a dynamic picture of the transport and selectivity mechanism at work in aquaporins is possible with molecular dynamics (MD) simulations. In MD simulations, the forces between the different atoms in a system are computed, and the atoms are then allowed to move under the influence of these forces. This allows the sampling of different conformations of the molecule being studied, including conformations that are crucial in driving biological phenomena like water transport. Simulation studies have provided insights into a number of aspects of aquaporins, including the role of the asparagine-proline-alanine (NPA) motif and the aromatic/arginine (ar/R) constriction, water transport mechanism, mechanisms defining the selectivity of the channel, interaction with lipids, response to external electric field, and binding of putative drug molecules. This chapter provides a brief review of the current status of computational modeling of aquaporins using MD simulations. Initially, a brief account of force fields and MD simulations is presented followed by an account of how MD simulations have contributed to further our understanding of different aspects of aquaporins.