Low code molecular property prediction

A toolkit for working with coarse-grain systems

Chem-MRL: SMILES-based Matryoshka Representation Learning Embedding Model

Draw molecules with plotly!

pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/

A modular drug-design AI Agent

Program to survey all possible molecular structures of fragment ion based on exact-mass and molecular formula of it. Ideal for ESI-Tandem MS/MS fragment's structure studies.

A Python library for efficient manipulation, conformer optimization, and molecular structure data.

ChemBERTa + RL pipeline for de novo EGFR inhibitor design with docking validation.

MCP server enabling AI agents to search for synthesizable building blocks and screening compounds

Collect adsorption isotherm data from the NIST/ARPA-E Database and train a ML model to predict uptake from pressure

GNN powered visualize solutions to SMILE(simplified Molecularinputline entrysystem)

⬢⬢⬢ Organizing and processing tables of chemical structures.

Access online services of chemical identifiers from Python

Generate synthetic chemical reactions from a retrosynthetic SMARTS template and a stock of molecules.

ModChemBERT: ModernBERT as a Chemical Language Model

Generates an SDF conformer ensemble from SMILES. This script creates an initial pool of 250 conformers (using RDKit ETKDG), optimizes them (MMFF94), and then applies a greedy diversity filter. The final ensemble contains a subset of conformers selected for both their low energy and geometric diversity (RMSD threshold).

Многомодальная нейросетевая система для предсказания биоактивности молекул и генерации новых структур с заданными свойствами. Проект разработан для поиска потенциальных сокристаллов теофиллина с использованием генеративных моделей и эволюционной оптимизации.

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS

⚗️ An all-in-one solution for chemical property retrieval from PubChem.