102 citations · 213 across the 9 of their papers we have counts for
9 papers
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17
General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks…
nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder
Maksim Kuznetsov, Airat Valiev, Alex Aliper +4
Recent advancements have integrated Language Models (LMs) into a drug discovery pipeline. However, existing models mostly work with SMILES and SELFIES chemical string representatio…
nach0: Multimodal Natural and Chemical Languages Foundation Model
Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina +8
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creat…
Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer
Zulfat Miftahutdinov, Artur Kadurin, Roman Kudrin +1
Concept normalization in free-form texts is a crucial step in every text-mining pipeline. Neural architectures based on Bidirectional Encoder Representations from Transformers (BER…
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov +3
The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named…
CommentsRadar: Dive into Unique Data on All Comments on the Web
Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov +1
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites. The engin…
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
Zulfat Miftahutdinov, Elena Tutubalina
In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled v…
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko +1
In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usual…
An Encoder-Decoder Model for ICD-10 Coding of Death Certificates
Elena Tutubalina, Zulfat Miftahutdinov
Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest. The task of medical concept coding…