There are 2 repositories under smiles-strings topic.
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.
QSARtuna: QSAR model building with the optuna framework
The official codebase of the paper "Chemical language modeling with structured state space sequence models"
Get chemical SMILES strings (structures) based on the CAS numbers or the names of the chemicals.
PSP is a python toolkit for predicting atomic-level structural models for a range of polymer geometries.
tools to perform group contribution (GC) identification, given the SMILES of a compound
Python interface for Enhanced Monte Carlo (EMC)
Variational Autoencoder (VAE)-based molecular SMILES string generator
Taffi component increment theory used to predict enthalpy of formation, standard entropy and heat capacity.
11th place solution of NeurIPS 2024 - Predict New Medicines with BELKA competition on Kaggle: https://www.kaggle.com/competitions/leash-BELKA
deep learning based prediction of structures and functional groups from MS/MS spectra
Smilez is a simple compression library for SMILES strings.
SMILES, SELFIES and Reaction SMILES augmentation using RDKit
Encoder-decoders for translating different chemical formats.
A web application to track the kinase research done by the SGC.
Script developed to transform the amino acid smiles to one letter code for later analysis
A command-line tool for simple, single-step retrosynthetic reaction prediction using graph partitioning.
Training pre-trained BERT language model on molecular SMILES from the Molecule Net benchmark by leveraging mixup and enumeration augmentations.
Prediction of Henry’s Law Constants using descriptors calculated from simple molecular representations
This repository serves as a comprehensive toolkit for performing cheminformatics calculations in the context of drug discovery. It provides a range of algorithms, tools, and code snippets tailored to assist researchers and professionals working in the field of pharmaceuticals and drug development.
This project implements a high-performance lossless codec for SMILES strings, designed for efficient compression and decompression. It's compatible with CUDA and AMD's HIP framework
A Variational Autoencoder in Google Colab to generate and visualize novel molecular structures for potential drug discovery applications, using the QM9 dataset and SMILES representation.
Code to retrieve drugs against a desired target using the ChEMBL database API
Binary string classification of SMILES with an LSTM
The official codebase of the paper "Deep Supramolecular Language Processing for Co-crystal Prediction"
This Streamlit app allows you to search for the most "druglike" molecules basing on the "Rule of five"
A transformer-based molecular optimization framework. Optimize any molecule using an arbitrary scoring function while maintaining its integrity and purpose.
Prediction of LogP from SMILES datasets of chemical molecules
A collections of basic autoencoders and Generative models for chemistry
Drug discovery with ML and DL approach
Python codes of ASCII Text Based Chemical Structure Tuner (ACST): String Reversal Mechanism for SMILES Notation.
Bio-cheminformatics project in progress to predict small molecule inhibitory potential (IC50) against 11β-HSD1