AI-based language models powering drug discovery and development

Abstract

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

AI-powered drug repurposing for developing COVID-19 treatments

“…Additionally, as of October 19, 2021, over 6800 COVID-19 clinical trials are listed on the clinicaltrials.gov website, providing details such as study design, interventions, trial locations, and phase status ( https://clinicaltrials.gov/ct2/results?cond=COVID-19 ). These resources can help researchers generate different repurposing hypotheses by taking full advantage of AI-powered language models to mine valuable information to facilitate AI-powered drug repurposing development ( Liu et al, 2021a ).…”

“…AI-powered language models (LMs) have changed the landscape of NLP fields. Notably, different transformer based LMs such as BERT and its derivatives outperformed state-of-the-art NLP approaches in various NLP tasks, including text classification, named entity recognition (NER), question and answering (Q&A), and text summarization ( Liu et al, 2021a ). Zhang et al (2021) proposed literature-derived knowledge and knowledge-graph completion methods using BERT and five different neural knowledge-graph completion algorithms for COVID-19 repurposing.…”

“Artificial Intelligence” for Pharmacovigilance: Ready for Prime Time?

“…While there are challenges in automating the extraction of the details about these features from ICSR narratives (e.g., the signs and symptoms needed to apply a case definition), the larger issue is that the cognitive processes for feature integration are complex, primarily conducted through global introspection, iterative in nature (as reflected by the circular arrows at the center of the figure), and not defined in sufficient detail to make computable. Table 1 categorizes the efforts FDA has taken over the past decade to apply AI to this complex process [ 29 , 36 – 61 ]. As can be seen in the description of the efforts, most have involved automating the extraction of the key features from ICSR narratives using NLP, with a few attempting to develop predictive ML algorithms that attempt to automate the cognitive processes for feature integration.…”

“…In the production setting, extraction of key features (e.g., age) from the ICSR narrative has been implemented; integration into traditional workflows and IT systems of more complex algorithms, such as identifying and removing duplicate ICSRs based on both structured fields and narrative text, is underway. Development of a general platform that breaks down the case evaluation process into computable steps and would allow for insertion of improved algorithms for a given task (e.g., automating the application of a case definition) is an active area of research [ 59 ], along with application of AI-based language models to ICSR narratives to improve extraction of key features and their relationships [ 60 , 61 ].…”

How can natural language processing help model informed drug development?: a review

“… 5 In the field of MIDD, NLP can be leveraged to extract information out of structured (eg, electronic health records [EHRs]) and unstructured (eg, research documents) data to optimize and/or accelerate various processes in the drug development lifecycle, eg, determining drug–target interaction 6 and drug–drug interaction, 7 biomarker discovery, 8 drug repurposing, 9 , 10 patient-trial matching, 11 model-based meta-analysis, 12 disease progression modeling, 13 and others. 14 NLP platforms perform the role of assessing potential associations between chemical/drug entities, their target proteins, and novel disease-related pathways by extensive analysis of scientific literature. NLP can also accelerate repurposing of approved drugs for new diseases which enables pharmacologists to address new market at a fraction of cost and time.…”