Exploring the Use of ChatGPT in Life Science
Updated: Feb 7
ChatGPT: a new chat system based on artificial intelligence from the company Open AI, became a viral phenomenon within a small span of time. Its potential, capabilities, and future usage are being discussed across the globe. Here at Vikasietum, we have started exploring it and we believe it can be useful in the Life Science field to obtain great results.
Natural language processing (NLP) models can be used to extract information from scientific literature, such as identifying potential drug targets and understanding disease mechanisms. For example, researchers have used NLP models to extract information about protein-protein interactions from scientific literature, which can be used to develop new drugs.
NLP models can be used to predict the chemical properties and interactions of potential drug compounds, assist in the design of experiments, and help interpret results. For example, researchers have used NLP models to predict the efficacy of drugs based on their chemical structure and interactions with biological targets.
NLP models can be used to analyze large sets of genetic and genomic data, such as identifying genetic variants associated with diseases. For example, researchers have used NLP models to extract information about genetic mutations from scientific literature and link them to specific diseases.
Clinical Trial Recruitment
NLP models can be used to improve the efficiency of clinical trial recruitment by identifying eligible patients from their electronic health records. For example, researchers have used NLP models to extract information about patients' medical history, demographic data, and disease status from electronic health records and match them with clinical trial criteria.
Protein Structure Prediction
NLP models can predict the structure of proteins from their amino acid sequence, which can be helpful for drug discovery.
Biomedical Named Entity Recognition
NLP models can be used to identify specific entities in a biomedical text like genes, proteins, diseases, drugs, and others.
NLP models can be used to predict protein-ligand interactions, predict protein stability, and predict protein-protein interactions.
Overall, the use of NLP models in life sciences has the potential to accelerate the drug discovery process and improve our understanding of the underlying mechanisms of diseases.