NLP Expert for a bioinformatics engineering project

PREPAIRE-NLP Division We will pay accordingly to experience, and work. Looking for the best of the best. More details of the scope for the project and company will be shared upon further discussion, this is a great opportunity ahead. Possibility for full employment for those who perform if interested after. We need an NLP expert for the following tasks: Task: 1) For a given medical paper, extract a set of strings and classify them by categories. (2DChemical Structure, 3D Chemical Structure, Absorption, Abstract, ADMET, Age, Age Group, Aminoacid, Residues, Antibacterial, Antioxidant, Antiparasites, Antiviral Agent, AUC, Author, Bacteria, Binding Energy, Bioactive components, Bioavailability, Biodegradation, Blood Brain Barrier (BBB), Body Mass Index (BMI), CaCo2, Probability, CAS Number, Clearence, Clinical Dose, Clinical Trial Phase, Cmax, Conclusion, Demography, Disease, Dosage, Dose, Drug, Drug, Efficacy, Drug–Drug Interaction, EC50, EC90, Female ,Gene, Ghose Filter, Half Life, Height, Human Intestinal Absorption (HIA), Hydrogen Acceptor Count, Hydrogen Donor Count, IC50, IC90, In vitro permeability, IUPAC Name, Ligand, Lipophilicity, logP, logs, Maintanence Dose, Male, Maximal Tolerable Dose (MTD), Mechanism of Action, Metabolism, metabolites, Minimum Inhibitory Concerntration(MIC), Molecular Weight, Nationality, Pathogen, Pathogenesis, Pathway, Peptides, Pharmacodynamics, Phenolic Compounds, Phytochemical, pKa (Strongest Acidic), pKa (Strongest Basic), Polar Surface Area, Potential Inhibitor, Protein, Route of elimination, Rule of Five, Sequeale, Side Effect, Stability, Target, Target plasma, concentration (TLC), Title , TMax, Toxicity, Virus,, Volume of Distribution, Water solubility, Weight ) 2) Building an NLP model to extract the above labels information automatically from newly published articles. 3) Once the above information is extracted then the next phase will be building the NLP model. The task of NLP model will be below: a) Finding the interaction between drug-drug, drug-protein and drug-disease. b) Representing the interaction(drug-drug, drug-protein and drug-disease) in the form of a knowledge graph. c) Finding the relationship between drugs, disease and proteins and representing them in the knowledge graph. d) Semantic based search such as suggesting the drugs based on the disease, age, demographic etc. 4) Other Important NLP Task a) Name Entity Recognition : Tagging drug, disease and proteins in article data b) Span Categorization: Extracting longer phrases and nested expressions from articles. c) Text Classification: Such as drugs review classification or set of lines into the classification d) Depencies and Relations : Drug and disease dependency, two disease relation etc 5) NLP Model Which need to be applied : BERT, GPT-3, XLNet, RoBERTa, ALBERT, Word2Vec, Deep Generative Model etc.