The lack of recommended drugs or vaccines to deal with the COVID-19 is the main concern of this pandemic. The approved drugs for similar health problems, drugs under clinical trials, and molecules from medicinal plants extracts are investigated randomly to deal with the COVID-19 infection. Molecular docking, one of the best approach to search therapeutically potent drugs/molecules in real time with possible hope to apply on COVID-19. In this communication, molecular docking studies of 18 ligands were carried out with the three therapeutic target proteins of SARS-CoV-2, i.e., RNA-dependent RNA polymerase (RdRp), angiotensin-converting enzyme 2 (ACE2) and spike glycoprotein (SGp). The obtained results revealed that the phytochemicals showed better dock score in compared to the drugs paracetmol and hydroxychloroquine. Combining the dock score and medicinal properties, we believe the terpenoids based phytochemicals limonin and scopadulcic acid B can be further explored for potential use against COVID-19.
Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing faci…
Furin is a serine protease that takes part in the processing and activation of the host cell pre-proteins. The enzyme also plays an important role in the activation of several viruses like the newly emerging SARS-CoV-2 virus that causes COVID-19 disease with a high rate of virulence and mortality. Unlike viral enzymes, furin owns a constant sequence and active site characteristics and seems to be a better target for drug design for COVID-19 treatment. Considering furin active site as receptor and some approved drugs from different classes including antiviral, antibiotics, and anti protozoa/ant…
Global coronavirus disease pandemic (COVID-19) caused by newly identified SARS- CoV-2 coronavirus continues to claim the lives of thousands of people worldwide. The unavailability of specific medications to treat COVID-19 has led to drug repositioning efforts using various approaches, including computational analyses. Such analyses mostly rely on molecular docking and require the 3D structure of the target protein to be available. In this study, we utilized a set of machine learning algorithms and trained them on a dataset of RNA-dependent RNA polymerase (RdRp) inhibitors to run inference anal…
The world has witnessed unprecedented human and economic loss from the COVID-19 disease, caused by the novel coronavirus SARS-CoV-2. Extensive research is being conducted across the globe to identify therapeutic agents against the SARS-CoV-2. Here, we use a powerful and efficient computational strategy by combining machine learning (ML) based models and high-fidelity ensemble docking simulations to enable rapid screening of possible therapeutic molecules (or ligands). Our screening is based on the binding affinity to either the isolated SARS-CoV-2 S-protein at its host receptor region or to th…
Alzheimer disease (AD) is the leading cause of dementia, accounts for 60 to 80 percent cases. Two main factors called beta amyloid plaques and tangles are prime suspects in damaging and killing nerve cells. However, oxidative stress, the process which produces free radicals in cells, is believed to promote its progression to the extent that it may responsible for the cognitive and functional decline observed in AD. As of today there are few FDA approved drugs in the market for treatment, but their cholinergic adverse effect, potentially distressing toxicity and limited targets in AD pathology …
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected near 5 million people and led to over 0.3 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than ten years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental dataset for SARS-CoV-2 or SARS-CoV main protease inhibitors. Based on this dataset, we develop validated machine learning models with relatively low root…
Information on protein-protein interactions (PPIs) not only advances our understanding of molecular biology but also provides important clues for target selection in drug discovery and the design of PPI inhibitors. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. However, a friendly interface for users who are not sufficiently familiar with the command line interface has not been developed so far. In this study, we have developed a graphical user interface, MEGADOCK-GUI, which enables users to…
Drug repurposing is an unconventional approach that is used to investigate new therapeutic aids of existing and shelved drugs. Recent advancement in technologies and the availability of the data of genomics, proteomics, transcriptomics, etc., and with the accessibility of large and reliable database resources, there are abundantly of opportunities to discover drugs by drug repurposing in an efficient manner. The recent pandemic of SARS-COV-2, that caused the death of 6,245,750 human beings to date, has tremendously increase the exceptional usage of bioinformatics tools in interpreting the mole…
IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research Europe（IBM欧洲研究院）
The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules f…
Currently, there is no effective antiviral drugs nor vaccine for coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its high conservativeness and low similarity with human genes, SARS-CoV-2 main protease (M$^{\text{pro}}$) is one of the most favorable drug targets. However, the current understanding of the molecular mechanism of M$^{\text{pro}}$ inhibition is limited by the lack of reliable binding affinity ranking and prediction of existing structures of M$^{\text{pro}}$-inhibitor complexes. This work integrates mathematics and deep lea…
The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific …
The novel coronavirus (2019-nCoV) has been declared to be a new international health emergence and no specific drug has been yet identified. Several methods are currently being evaluated such as protease and glycosylated spike (S) protein inhibitors, that outlines the main fusion site among coronavirus and host cells. Notwithstanding, the Heptad Repeat 1 (HR1) domain on the glycosylated spike (S) protein is the region with less mutability and then the most encouraging target for new inhibitors drugs.The novelty of the proposed approach, compared to others, lies in a precise training of a deep …
Prenatal COVID infection is one of the worst affected and least attended aspects of the COVID-19 disease. Like other coronaviruses, CoV2 infection is anticipated to affect fetal development by maternal inflammatory response on the fetus and placenta. Studies showed that higher prenatal choline level in mother's body can safeguard the developing brain of the fetus from the adverse effects of CoV2 infection. Choline is commonly used as food supplement. By virtual screening, molecular docking and molecular dynamics techniques, we have established a strong inhibitory possibility of choline for SAR…
In the present study, we have described how by using molecular docking and molecular dynamic (MD) simulation studies the combination drug of ivermectin and doxycycline can be used as a potential inhibitor for SARS-CoV-2 virus. In lieu of unavailability of specific cure of COVID-19 till now various possibilities for individual and combination drugs have been explored by the medical practitioners/scientists for the remedial purpose of CoV-2 infections. $3CL^{pro}$ is the main protease of SARS-CoV-2 virus which plays an essential role in mediating viral replication in the human body. $3CL^{pro}$ …
Northeastern University（东北大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Scipher Medicine；Sabanci University（萨班哲大学）；Boston University（波士顿大学）；Central European University（中欧大学）
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SAR…
University College London（伦敦大学学院）；Università di Napoli Federico II（那不勒斯费德里科二世大学）；University of Chicago（芝加哥大学）；RWTH Aachen University（亚琛工业大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Rutgers, the State University of New Jersey（罗格斯大学，新泽西州立大学）；Science Museum（科学博物馆）；Italian Institute of Technology（意大利理工学院）；Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities（巴伐利亚科学与人文学院莱布尼茨超级计算中心）；CERN（欧洲核子研究中心）；Argonne National Laboratory（阿贡国家实验室）；University of Amsterdam（阿姆斯特丹大学）
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here…
IBM Research Europe（IBM欧洲研究院）；ETH Zurich（苏黎世联邦理工学院）
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems biology and drug discovery, we propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets. First, we train a multimodal ligand--protein binding affinity model on predicting affinities of antiviral compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a c…
Chemical Language Model Linker: blending text and molecules with modular adapters
Department of Computer Sciences, University of Wisconsin-Madison（威斯康星大学麦迪逊分校计算机科学系）；Drug Development Core, Small Molecule Screening Facility, University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison（威斯康星大学卡本纳癌症中心药物开发核心、小分子筛选设施）；Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物统计学与医学信息学系）
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系生物数据科学实验室）；Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系）；Dept. of Chemistry, Middle East Technical University（中东技术大学化学系）；Dept. of Physics, Middle East Technical University（中东技术大学物理系）；Dept. Of Artificial Intelligence Engineering, Bahcesehir University（巴切塞希尔大学人工智能工程系）；Cancer Systems Biology Lab, Graduate School of Informatics, Middle East Technical University（中东技术大学信息研究生院癌症系统生物学实验室）；Dept. of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University（加齐大学药学系药剂化学系）；Laboratory of Molecular Modeling, Evias Pharmaceutical R&D Ltd.（Evias制药研发有限公司分子建模实验室）；Dept. of Electrical and Electronics Engineering, İskenderun Technical University（伊斯肯德伦技术大学电子与电气工程系）；Institute for Computational Biomedicine, Heidelberg University（海德堡大学计算生物医学研究所）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（哈切塔佩大学健康科学研究生院生物信息学系）
