A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

WGAIN: Data Imputation using Wasserstein GAIN

Missing data is a well known problem in the Machine Learning world. A lot of datasets that are used for training algorithms contain missing values, e.g. 45% of the datasets stored in the UCI Machine Learning Repository [16], which is a commonly used dataset collection, contain missing values [11]. Handling these missing values can be crucial. When treating them in a wrong way, this can cause major errors in training the algorithms or even result in false prediction. Thus, the motivation of inventing an algorithm that computes these missing values for datasets is huge. As a result, more and more imputation methods have been introduced over the last years. One of them is a method called Generative Adversarial Imputation Nets (GAIN) [27], which is, as the name suggests, a imputation method based on Generative Adversarial Nets (GAN)[13]. In this method the GAN setting is adapted so that it can impute missing values based on the conditional distribution of the data rather than taking the expectation for a missing value. Nonetheless, this method suffers from similar problems that are appearing when training GAN, which are going to be described in this thesis. Knowing that the problems of GAN can be fixed using the Wasserstein distance, the focus of this thesis was to use the GAIN algorithm and adapt it by using the Wasserstein distance. The goal of this thesis was to proof that it is possible to use the GAIN algorithm together with the Wasserstein setting and apply it to impute missing values. Another goal of this thesis was to compare the newly implemented algorithm called WGAIN with already existing imputation methods. Therefore, the imputation performance was compared using the root mean squared error. Furthermore, the prediction performance of a logistic regression model trained on the, by various imputation methods, imputed data was compared using the area-under-the-curve value. Furthermore, the prediction performance of a logistic regression model trained on the imputed data by various imputation methods was compared using the area-under-the-curve value. It is shown in this thesis that imputing missing values using Wasserstein GANs is indeed an alternative method for missing data imputation and provides comparable results to other state-of-the-art imputation methods especially GAIN. It is also worth mentioning that the computational effort using WGAIN is considerable lower than using GAIN.submitted by Christina HalmichUniversität Linz, Masterarbeit, 2020(VLID)484113

A community effort to discover small molecule SARS-CoV-2 inhibitors

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery