Models and Processes to Extract Drug-like Molecules From Natural Language Text

Abstract

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

Drug Discovery in Canine Pyometra Disease Identified by Text Mining and Microarray Data Analysis

“…Therefore, an in-depth study of the key regulatory genes and drug discovery of signaling pathways is of great significance for the diagnosis and treatment of canine pyometra. Meanwhile, bioinformatics, as an emerging discipline, is used to deal with medical data to identify novel diagnosis markers and discover new drugs [12][13][14]. However, to date, no researchers have adopted this method to study the drug discovery of canine pyometra.…”

“…As a variety of research methods for bioinformatics, text mining and microarray data analysis have been applied to the screening of disease biomarkers, the identification of signaling pathways, and the discovery of new drugs [12][13][14][15][16]. At the same time, researchers have found a series of pathogenesis of canine pyometra by doing experiments [17][18][19].…”

AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text

“…The trained SpaCy and Keras models and the results of applying the models to the 198 875 articles in the CORD-19 corpus are also available in this GitHub repo. Additionally, the pretrained SpaCy model is provided as a cloud service via DLHub [13,18]. (The Keras model could not be hosted there due to compatibility issues with DLHub.)…”

“…The models used in this work and the datasets generated for this study can be found on GitHub at https://github.com/globus-labs/covid-nlp/tree/master/drug-ner. The models are also available on DLHub [18].…”