A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
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Updated
Sep 11, 2023 - Python
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.
A lightweight python-only library for reading and writing SMILES strings
QSARtuna: QSAR model building with the optuna framework
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.
Python interface for Enhanced Monte Carlo (EMC)
tools to perform group contribution (GC) identification, given the SMILES of a compound
De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)
Taffi component increment theory used to predict enthalpy of formation, standard entropy and heat capacity.
Smilez is a simple compression library for SMILES strings.
SMILES, SELFIES and Reaction SMILES augmentation using RDKit
deep learning based prediction of structures and functional groups from MS/MS spectra
Encoder-decoders for translating different chemical formats.
A web application to track the kinase research done by the SGC.
Training pre-trained BERT language model on molecular SMILES from the Molecule Net benchmark by leveraging mixup and enumeration augmentations.
A command-line tool for simple, single-step retrosynthetic reaction prediction using graph partitioning.
Script developed to transform the amino acid smiles to one letter code for later analysis
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 Streamlit app allows you to search for the most "druglike" molecules basing on the "Rule of five"
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