Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer

Miftahutdinov, Zulfat; Kadurin, Artur; Kudrin, Roman; Tutubalina, Elena

doi:10.1007/978-3-030-72113-8_30

Cited by 6 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset is the property of Insilico Medicine and is commercially available as a part of inClinico platform. All trials were mapped to therapies and conditions by a natural language processing (NLP) pipeline which is based on the state‐of‐the‐art Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer (DILBERT) 21–23 . This NLP pipeline incorporates two modules: (i) named entity recognition module; and (ii) entity linking module.…”

Section: Clinical Trial Dataset and Model Architecturesmentioning

confidence: 99%

Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi‐Modal Artificial Intelligence

Aliper¹,

Kudrin²,

Polykovskiy³

et al. 2023

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer‐based artificial intelligence software platform designed to predict the outcome of phase II clinical trials. The platform combines an ensemble of clinical trial outcome prediction engines that leverage generative artificial intelligence and multimodal data, including omics, text, clinical trial design, and small molecule properties. inClinico was validated in retrospective, quasi‐prospective, and prospective validation studies internally and with pharmaceutical companies and financial institutions. The platform achieved 0.88 receiver operating characteristic area under the curve in predicting the phase II to phase III transition on a quasi‐prospective validation dataset. The first prospective predictions were made and placed on date‐stamped preprint servers in 2016. To validate our model in a real‐world setting, we published forecasted outcomes for several phase II clinical trials achieving 79% accuracy for the trials that have read out. We also present an investment application of inClinico using date stamped virtual trading portfolio demonstrating 35% 9‐month return on investment.

show abstract

Section: Clinical Trial Dataset and Model Architecturesmentioning

confidence: 99%

Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi‐Modal Artificial Intelligence

Aliper¹,

Kudrin²,

Polykovskiy³

et al. 2023

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ambiguity problem is especially crucial for such domains as biomedical and clinical text processing due to variability of medical terms, the complexity of medical ontologies such as UMLS [12], and scarcity of annotated resources. There is a long history of development of EL tools for biomedical literature and electronic health record mining applications [6,24,83,101,109,156,168,178,209]. These tools have been successfully applied for summarization of clinical reports [104], extraction of drug-disease treatment relationships [81], mining chemical-induced disease relations [10], differential diagnosis [5], patient screening [41], and many other tasks.…”

Section: Established Applicationsmentioning

confidence: 99%

Neural entity linking: A survey of models based on deep learning

Sevgili

Shelmanov

Архипов

et al. 2022

View full text Add to dashboard Cite

This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the “deep learning revolution” in natural language processing. Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks. This work distills a generic architecture of a neural EL system and discusses its components, such as candidate generation, mention-context encoding, and entity ranking, summarizing prominent methods for each of them. The vast variety of modifications of this general architecture are grouped by several common themes: joint entity mention detection and disambiguation, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. Since many neural models take advantage of entity and mention/context embeddings to represent their meaning, this work also overviews prominent entity embedding techniques. Finally, the survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models based on the Transformer architecture.

show abstract

“…5 The application of neural network architectures and LMs has signicantly advanced the eld of chemistry, particularly in domain-specic information retrieval, drug development, and clinical trial design. [6][7][8][9][10][11][12][13][14][15] These developments include neural molecular ngerprinting, generative approaches to small molecule design, [11][12][13] prediction of pharmacological properties, and drug repurposing. 13,14 The clinical development of a drug is a time and money consuming process that typically requires several years and a billion-dollar budget to progress from phase 1 clinical trials to the patients.…”

Section: Introductionmentioning

confidence: 99%