Our project aims to revolutionize the process of advanced material development by leveraging cutting-edge technologies in natural language processing (NLP) and chemistry. By combining the power of AI-driven text generation with molecular modeling and analysis, our platform offers a comprehensive solution for predicting, designing, and optimizing novel materials with unprecedented speed and accuracy.
-
Multi-task Text and Chemistry T5: A Hugging Face model that integrates text and chemistry tasks, facilitating seamless interaction between textual data and chemical structures.
-
BioT5: A Hugging Face model specifically tailored for biomedical text generation, enabling advanced analysis and interpretation of biomedical data in the context of material development.
-
MolT5: A Hugging Face model designed for molecular generation and manipulation, providing powerful capabilities for molecular design and optimization in material science applications.
-
BioPandas: A Python library for manipulating and analyzing biomolecular data structures, enhancing our platform's capabilities for handling complex molecular structures and biochemical data.
-
rxn_yields: A tool for predicting reaction yields in organic chemistry, enabling accurate estimation of chemical reaction outcomes and product yields.
-
MolecularTransformer: A deep learning model for molecular property prediction and molecular optimization, facilitating the discovery of novel materials with desired properties.
-
BIMODAL: A framework for multi-modal molecular generation, allowing for the integration of textual and structural information to guide material design and synthesis.
-
ChemDoodle: A web-based platform for molecular sketching and visualization, providing intuitive tools for creating and analyzing molecular structures within our development environment.
This project is licensed under the MIT License.