Search Results for author:
Found 11 papers, 5 papers with code
Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting.
Exploiting Pretrained Biochemical Language Models for Targeted Drug Design
On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations.
Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models
The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Here, we introduce the application of balancing loss functions for multi-label text classification.
Ranked #1 on
Multi-Label Text Classification
on Reuters-21578
Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.
WideDTA: prediction of drug-target binding affinity
In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model.
ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction
Our aim is to process the patterns in SMILES as a language to predict protein-ligand affinity, even when we cannot infer the function from the sequence.
A novel methodology on distributed representations of proteins using their interacting ligands
We showed that ligand-based protein representation, which uses only SMILES strings of the ligands that proteins bind to, performs as well as protein-sequence based representation methods in protein clustering.
DeepDTA: Deep Drug-Target Binding Affinity Prediction
The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.
Ranked #1 on
Drug Discovery
on BindingDB IC50
