Open-source alternatives to Qmlcode Qml

molfeat - the hub for all your molecular featurizers Docs | Homepage

DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti

This is the computational appendix for the following paper:

license-image:https://img.shields.io/badge/license-GPL3.0-green.svg license-url:https://github.com/divelab/AIRS/blob/main/LICENSE contributing-image:https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat

](https://pepy.tech/project/mlatom)

Tensor network machine learning. Based on the paper "Supervised Learning with Quantum Inspired Tensor Networks" http://arxiv.org/abs/1605.05775

A library for easy and efficient manipulation of tensor networks.

The QUIP package is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to other software such as LAMMPS, CP2K and also…

maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

AtomAI is a Pytorch-based package for deep and machine learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by…

:atom_symbol: Qiskit (Quantum Information Science Kit) for JavaScript

Cirq is a Python quantum computing framework used for designing, simulating, and executing quantum circuits on Noisy Intermediate-Scale Quantum (NISQ) hardware. It serves as a quantum circuit simulator and noise modeler, as well as a tool for the implementation of quantum algorithms. The framework provides a specialized interface for NISQ hardware, allowing users to map logical quantum circuits to physical device topologies while validating hardware connectivity and gate constraints. It distinguishes itself through integrated noise modeling, applying depolarizing and damping channels to mimic