Bioactivity Prediction For Chemical Structures

via “chemical safety assessment and hazard prediction”

AI agent with chemistry tools for synthesis planning.

Unique: Integrates safety assessment directly into the agent's synthesis planning loop, allowing the LLM to proactively flag hazards and suggest safer alternatives during route planning. Unlike standalone safety databases, this capability is called on-demand as part of multi-step reasoning, enabling dynamic risk assessment.

vs others: More integrated into synthesis planning than standalone safety databases; however, relies on PubChem's hazard data which is less comprehensive than commercial safety systems (e.g., Sigma-Aldrich SDS databases) and lacks quantitative toxicity modeling.

via “ligand binding site prediction and pocket characterization”

* 📰 2022: [ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)](https://openai.com/blog/chatgpt/)

Unique: Combines geometric pocket detection (concavity analysis, volume calculation) with machine learning scoring trained on known drug-target complexes, enabling both pocket identification and druggability assessment in a single step. Residue-level hydrophobicity and charge analysis refines pocket characterization.

vs others: More comprehensive than simple concavity-based methods (e.g., POCASA); integrates druggability scoring to prioritize pockets likely to bind small molecules, reducing false positives from non-functional cavities.

via “protein-sequence-annotation-and-function-prediction”

A large language model for science. Can summarize academic literature, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more. [Model API](https://github.com/paperswithcode/galai).

via “molecular-interaction-prediction”

via “batch molecular property prediction”

via “molecular interaction prediction”

via “off-target binding prediction and toxicity assessment”

via “structure-activity-relationship-modeling”

via “protein activity and function prediction”

via “biological-sequence-prediction”

via “protein-structure-prediction”

via “rna-ligand interaction prediction”