Home AI News Advancements in LLMs: From ProGen to CancerGPT in Biology and Medicine

Advancements in LLMs: From ProGen to CancerGPT in Biology and Medicine

Advancements in LLMs: From ProGen to CancerGPT in Biology and Medicine

ProGen: Generating Protein Sequences with Proven Functions

ProGen is a powerful deep-learning model known as an LLM (Language Model) that has the capability to generate protein sequences with predictable functions. This model has been trained on a massive dataset of 280 million protein sequences from over 19,000 different families. To enhance its performance, ProGen is equipped with control tags that specify the properties of the proteins. It can also be fine-tuned using specific sequences and tags to create more accurate protein sequences.

ChemCrow: Boosting LLMs in Chemistry

While LLMs have excelled in various domains, they often struggle with chemistry-related challenges. Furthermore, these models don’t have access to external sources, which limits their usefulness in scientific research. That’s where ChemCrow comes in. It’s an LLM specifically designed for chemistry tasks such as drug discovery, organic synthesis, and materials design.

ChemCrow integrates 13 expert-designed tools to enhance its chemistry performance. This model can be a valuable tool for both expert chemists and non-experts, as it helps lower barriers and facilitate scientific advancements by bridging the gap between experimental and computational chemistry.

ChatGPT: Advancements in Drug Discovery

Researchers from Michigan State University have explored the use of ChatGPT in drug discovery and made some exciting discoveries:

1. ChatGPT can be fine-tuned on scientific literature to generate summaries of the latest research on a particular disease. This aids researchers in identifying new potential targets or gaining a better understanding of the current research landscape.

2. By training ChatGPT on established drug-like molecules, it’s possible to generate novel chemical structures with similar characteristics. This can help scientists identify new lead compounds with higher success rates in clinical studies.

3. ChatGPT can predict the pharmacokinetics and pharmacodynamics of new drugs, supporting the virtual screening of chemical libraries in early-stage drug discovery.

4. ChatGPT can be trained on toxicity data to predict the potential toxic effects of new drugs.

Using ChatGPT/GPT-4 to Optimize Computational Biology Workflows

Computational biologists can benefit greatly from using ChatGPT/GPT-4 in their workflows. Here are some ways it can be utilized:

1. Improving code readability and documentation.

2. Assisting in writing efficient code.

3. Integrating ChatGPT into IDEs (RStudio and Visual Studio Code) via plugins.

4. Enhancing scientific writing and helping express ideas more clearly.

5. Cleaning and reconciling data.

6. Suggesting new visualization techniques and improving data visualization.

7. Fine-tuning the GPT API for specific applications and adjusting parameters for response creativity and repetitiveness.

ChatGPT in Bioinformatics: Assisting Students and Predicting Drug Interactions

ChatGPT has proven useful in bioinformatics education. It can help students generate code for scientific data analysis tasks such as aligning short reads to the human reference genome or creating phylogenetic trees.

Additionally, ChatGPT has shown promise in predicting and explaining common Drug-Drug Interactions (DDIs). While it can provide information to patients about DDIs, further improvement is needed to ensure complete guidance.

ChatGPT in Pharmacometrics: Enhancing Data Analysis

ChatGPT has several use cases in pharmacometrics:

1. Accurately obtaining typical PK parameters from scientific literature.

2. Generating population PK models in R.

3. Developing interactive Shiny applications for visualization.

4. Simplifying R code development and debugging.

GeneGPT and CancerGPT: Harnessing the Power of LLMs in Genomics and Cancer Research

GeneGPT is a method that teaches LLMs to utilize the NCBI Web API for answering genomics questions. It has achieved state-of-the-art results in genomics tasks.

CancerGPT is a few-shot learning model for predicting drug pairs synergy in rare tissues. It’s comparable to larger fine-tuned models and offers an alternative approach for biological inference in cancer research.

ChatGPT in Medical Research and Medicine

ChatGPT can analyze large volumes of data, extract relevant information, and present it in a structured form. This model can assist in creating new hypotheses and developing clinical decision-support systems based on patient records.

Moreover, ChatGPT is handy for providing information on the latest literature, writing discharge summaries, and aiding with patient discharge notes, trials summaries, and ethical guideline information.

These AI models have the potential to revolutionize various aspects of research and application in AI. They offer new possibilities for scientists, researchers, and even patients in the field of medicine and beyond.

[References to original articles have been provided for further reading.]

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