ProGen

Progen is a deep-learning LLM able to generating protein sequences with a predictable function across large protein families. ProGen was trained on 280M protein sequences from greater than 19,000 families, and the model is augmented with control tags specifying the property of the protein. ProGen might be fine-tuned to create more accurate protein sequences using specific sequences and tags.

ChemCrow

Although LLMs have shown great performance in tasks across various domains, they often struggle with chemistry-related problems. Moreover, these models should not have access to external sources, which limits their usefulness in scientific research. ChemCrow is an LLM chemistry agent that goals to unravel this issue. The model is designed to perform tasks across drug discovery, organic synthesis, and materials design.

13 expert-designed tools have been integrated to develop ChemCrow, which augments its performance in chemistry. The model has the power to help expert chemists and lower barriers for non-experts. Furthermore, It could actually facilitate scientific advancement by bridging the gap between experimental and computational chemistry.

ChatGPT in Drug Discovery

Researchers from Michigan State University have explored the usage of ChatGPT in drug discovery. They’ve provide you with the next results:

• ChatGPT might be fine-tuned on scientific literature and might be used to generate summaries of the most recent research on a given disease. This can assist researchers discover recent potential targets or higher understand the present state of research in a selected area. 
• By training ChatGPT on a set of established drug-like molecules, it is feasible to provide novel chemical structures with similar characteristics. This approach can assist scientists discover recent lead compounds with the next success rate in pre-clinical and clinical studies.
• ChatGPT can predict the pharmacokinetics and pharmacodynamics of recent drugs and support the virtual screening of chemical libraries in early-stage drug discovery.
• ChatGPT might be trained on a dataset of toxicity data after which used to predict the potential toxic effects of recent drugs.

Use of ChatGPT/GPT-4 in Computational Biology

Following are a number of the ways in which computational biologists can optimize their workflow using ChatGPT/GPT-4:

• Code readability and documentation might be improved using ChatGPT.
• ChatGPT can assist in writing efficient codes.
• Researchers can integrate ChatGPT into their IDEs via plugins for RStudio and Visual Studio Code.
• ChatGPT can improve scientific writing by providing aid in expressing ideas more clearly.
• ChatGPT might be used for cleansing and reconciling data.
• Data visualization might be improved as ChatGPT can suggest recent visualization techniques and enhance existing figures.
• The GPT API might be used to fine-tune the system for specific applications, and parameters might be adjusted to regulate the creativity and repetitiveness of responses.

ChatGPT in Bioinformatics

A bunch of researchers has demonstrated the feasibility of using ChatGPT in bioinformatics education to help students in generating code for scientific data evaluation tasks. Of their study, ChatGPT generated code to align the short reads to the human reference genome and summarized the alignments into count numbers across the genome. 

ChatGPT may assist students in phylogenetic analyses. The researchers created a phylogenetic tree for nine species using R code generated by the model. Of their study, the researchers also showed that ChatGPT could act as a virtual teaching assistant to show the divide-and-conquer approach to a student.

ChatGPT in Drug Development

A bunch of researchers demonstrated the effectiveness of ChatGPT in predicting and explaining common Drug-Drug Interactions (DDI). They prepared a complete of 40 DDIs list from previously published literature. Their study showed that ChatGPT is partially effective in predicting and explaining DDIs. 

Patients, who should not have immediate access to the healthcare facility, may take help from ChatGPT to get details about DDIs. Nevertheless, occasionally, the model may provide incomplete guidance. Due to this fact further improvement is required for potential usage by patients to get ideas about DDI.

ChatGPT in Pharmacometrics

Following are the use cases of ChatGPT in pharmacometrics:

• ChatGPT can accurately obtain typical PK parameters from the scientific literature.
• The model can generate a population PK model in R.
• ChatGPT is able to developing an interactive Shiny application for visualization.
• Using ChatGPT, R code might be developed with minimal coding knowledge. Furthermore, debugging of errors might be easily done using the identical.

GeneGPT is a novel method for teaching LLMs to utilize the National Center for Biotechnology Information (NCBI) Web API for answering genomics questions. GeneGPT has achieved state-of-the-art results on 75% of one-shot tasks and 80% of zero-shot tasks within the GeneTuring dataset. GeneGPT can potentially augment LLMs with domain tools to enhance access to biomedical information.

CancerGPT is a first-of-its-kind few-shot learning model that utilizes LLMs to predict the drug pairs synergy in rare tissues lacking structured data and features. It accommodates around 124M parameters and is even comparable to the larger fine-tuned GPT-3 model with 175B parameters. CancerGPT shows the potential of LLMs to supply an alternate approach for biological inference.

ChatGPT in Medical Research 

ChatGPT can analyze large volumes of knowledge, including scientific articles, medical reports, and patient reports. All of this evaluation can provide recent insights into the symptoms and treatment options for orthopedic conditions. 

ChatGPT can extract relevant information from the text and present it in a structured form. ChatGPT may assist within the creation of recent hypotheses for researchers. Moreover, ChatGPT might be useful in developing clinical decisions and support systems by analyzing patient records and identifying common patterns.

ChatGPT in Medicine

ChatGPT can inform researchers concerning the latest literature in a given area. It could actually write a discharge summary for patients following surgery. The model can aid with patient discharge notes, summarize recent trials, provide information on ethical guidelines, etc.

Don’t forget to affix our 20k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the most recent AI research news, cool AI projects, and more. If you’ve gotten any questions regarding the above article or if we missed anything, be at liberty to email us at Asif@marktechpost.com

🚀 Check Out 100’s AI Tools in AI Tools Club

References:

• https://www.nature.com/articles/s41587-022-01618-2
• https://arxiv.org/abs/2304.05376
• https://chemrxiv.org/engage/chemrxiv/article-details/63d56c13ae221ab9b240932f
• https://arxiv.org/abs/2303.16429
• https://www.biorxiv.org/content/10.1101/2023.03.07.531414v1.abstract
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105894/
• https://europepmc.org/article/ppr/ppr650004
• https://arxiv.org/abs/2304.09667
• https://arxiv.org/abs/2304.10946
• https://link.springer.com/article/10.1007/s00167-023-07355-6
• https://link.springer.com/article/10.1007/s11845-023-03377-8