Abstract

The pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers exploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using social media has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out relevant discussions from a huge corpus of messages and posts. Social media usage varies with internet penetration and other socioeconomic factors, which might induce disparity in analyzing discussions across different geographies. However, print media is a ubiquitous information source, irrespective of geography. Further, topics discussed in news articles are already newsworthy, while on social media newsworthiness is a product of techno-social processes. Developing this fundamental difference, we study Twitter data during the second wave in India focused on six high-population cities with varied macroeconomic factors. Through a mixture of qualitative and quantitative methods, we further analyze two Indian newspapers during the same period and compare topics from both Twitter and the newspapers to evaluate situational awareness around the second phase of COVID on each of these platforms. We conclude that factors like internet penetration and GDP in a specific city influence the discourse surrounding situational updates on social media. Thus, augmenting …

Abstract

Application of deep learning techniques for de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in natural language processing, such as Transformers, for molecular design in general. Inspired by generative pre-training (GPT) models that have been shown to be successful in generating meaningful text, we train a transformer-decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, MolGPT, performs on par with other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique, and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to

Abstract

The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on combinatorial methods to generate new substances often complimented by computational methods. The sheer size of the chemical space makes it infeasible to search through all possible molecules exhaustively. This calls for fast and efficient methods to navigate the chemical space to find substances with desired properties. This class of problems is referred to as inverse design problems. There are a variety of inverse problems in chemistry encompassing various subfields like drug discovery, retrosynthesis, structure identification, etc. Recent developments in modern machine learning (ML) methods have shown great promise in tackling problems of this kind. This has …

Abstract

Drug discovery is a long, expensive, and extremely laborious process that involves multiple steps with knowledge from a wide variety of domains like chemistry, biology and pharmacology. The first step in this process is the identification of potential hit molecules for a novel target followed by experimental evaluation typically using biochemical assays toward lead identification. These hits are then optimized to have higher binding affinity, low toxicity, and improved bioavailability among other requirements. The time and expense involved in this process has given rise to alternate in silico approaches like virtual screening wherein molecules are computationally evaluated to identify potential hits. The structure based drug design (SBDD) method, docking, is used most commonly in virtual screening to identify molecules with high binding affinity to the given target (1–

Abstract

Artificial Intelligence has seen a significant resurgence in the past decade in wide ranging technology and domain areas. Recent progress in digitisation and high influx of biomedical data have led to an unparalleled success of Machine Learning systems in healthcare, which is perceived to be a possible game changer for ‘healthcare to all’. This article gives an account of some of the current applications of AI solutions in the medical domains of diagnosis, prognosis and treatment. The article will also illustrate the implications of AI in the fight against the COVID-19 pandemic. Lastly, the article will summarise the challenges AI currently faces in its wide-scale adoption in the healthcare industry and how they can possibly be dealt with to move towards a more intelligent medical future. This may enable moving towards quality healthcare for all.

Abstract

There has been tremendous advancement in machine learning (ML) applications in computational chemistry, particularly in neural network potentials (NNP). NNPs can approximate potential energy surface (PES) as a high dimensional function by learning from existing reference data, thereby circumventing the need to solve the electronic Schrödinger equation explicitly. As a result, ML accelerates chemical space exploration and property prediction compared to quantum mechanical methods. Novel ML methods have the potential to provide efficient means for predicting the properties of molecules. However, this potential has been limited by the lack of standard comparative evaluations. In this work, we compare four selected models, that is, ANI, PhysNet, SchNet, and BAND‐NN, developed to represent the PES of small organic molecules. We evaluate these models for their accuracy and transferability on two

Abstract

Protein–drug interactions play important roles in many biological processes and therapeutics. Predicting the binding sites of a protein helps to discover such interactions. New drugs can be designed to optimize these interactions, improving protein function. The tertiary structure of a protein decides the binding sites available to the drug molecule, but the determination of the 3D structure is slow and expensive. Conversely, the determination of the amino acid sequence is swift and economical. Although quick and accurate prediction of the binding site using just the sequence is challenging, the application of Deep Learning, which has been hugely successful in several biochemical tasks, makes it feasible. BiRDS is a Residual Neural Network that predicts the protein’s most active binding site using sequence information. SC-PDB, an annotated database of druggable binding sites, is used for training the network …

Abstract

A system and method for exploring a chemical space during molecular design for at least one top hit molecule using a machine learning (ML) model are provided. The method includes (i) representing the at least one molecule stored in a drug library into at least one vector;(ii) clustering the at least one vector to obtain at least one cluster of molecules into one or more clusters;(iii) uniformly sampling a first subset of molecules from each cluster of molecules;(vi) determining a docking score for sampled subset of molecules;(iv) training the ML model by correlating sampled subset of molecules with docking score;(viii) computing acquisition function values for a second subset of molecules from each cluster; and (ix) determining at least one top hit molecule based on the computed acquisition function values, thereby exploring the chemical space for the at least one top hit molecule.

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.