Google and Drug Discovery

From Jun 2024 Mobile Health News

Google Research and Google DeepMind recently released a paper announcing the creation of a new LLM for drug discovery and therapeutic development dubbed Tx-LLM, fine-tuned from PaLM-2.

Tx-LLM utilizes the tech giant’s PaLM-2, its generative AI technology that uses Google’s LLMs to answer medical questions.

The drug discovery-focused LLM was trained using 709 datasets to target 66 tasks across the various stages of drug discovery, including evaluating efficacy and safety, predicting targets, and predicting ease of manufacturing.

The LLM constructs the Therapeutics instruction Tuning (TxT) collection by interleaving free-text instructions with representations of small molecules, such as SMILES strings for small molecules.

SMILES, or Simplified Molecular Input Line Entry System, is a typographical method using printable characters that represent molecules and reactions.

TxT was then used to prompt and fine-tune Tx-LLM, the therapeutics large language model, to solve classification, regression and generation tasks involved with drug discovery and therapeutic development.

To use TxT to predict drug synergy, the researchers used prompts composed of instructions, context and a question.

Tx-LLM performed above or near the state of the art (SOTA) models for 43 out of 66 tasks, and exceeded SOTA models on 22 tasks.