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+
+### Total Synthesis Guidance for Chemists
+
+#### Introduction: <br>
+Total synthesis refers to the process of constructing a complex organic molecule often
+with biological activity from simpler, commercially available or naturally occurring starting
+materials in the fewest steps possible and high yield. Here, two recent synthetic chemistry
+papers published in JACS and Nature Communications were analyzed with Large Multimodal
+Models (LMMs) so that a chemist can more easily reproduce or make the desired
+compound(s) more pure, faster, or less expensive in their own lab.
+
+#### Procedure: <br>
+Each paper was read manually along with corresponding supplementary and peer
+review documents. Prompts with keys were created for LMMs that asked specific information
+beyond what was provided by authors to potentially increase the yield of pure products in
+addition to locating exactly where the information was found that supported its conclusion.
+The LMM or Fine-tuned models were tested on three prompts per paper and included
+ChatGPT 4o, Organic Chem Scholar, Chemistry Chem, Scholar GPT, and Scholar AI.
+Llama 3.1 405B and Nemotron 4 340B large language models were also used to
+generate responses on how to make the total synthesis supplementary methods faster or
+less expensive than used in original papers, and were judged by a Cohere for AI model.
+
+August 01, 2024
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14968133.svg)](https://doi.org/10.5281/zenodo.14968133)
+
+---
+
+## New AI Drug Discovery &nbsp; [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13273141.svg)](https://doi.org/10.5281/zenodo.13273141)
+LLM Drug Discovery Applications