Best AI tools for< Computational Chemist >

Variational AI is a company that uses generative AI to discover novel drug-like small molecules with optimized properties for defined targets. Their platform, Enki™, is the first commercially accessible foundation model for small molecules. It is designed to make generating novel molecule structures easy, with no data required. Users simply define their target product profile (TPP) and Enki does the rest. Enki is an ensemble of generative algorithms trained on decades worth of experimental data with proven results. The company was founded in September 2019 and is based in Vancouver, BC, Canada.

XtalPi is a world-leading technology company driven by artificial intelligence (AI) and robotics to innovate in the fields of life sciences and new materials. Founded in 2015 at the Massachusetts Institute of Technology (MIT), the company is committed to realizing digital and intelligent innovation in the fields of life sciences and new materials. Based on cutting-edge technologies and capabilities such as quantum physics, artificial intelligence, cloud computing, and large-scale experimental robot clusters, the company provides innovative technologies, services, and products for global industries such as biomedicine, chemicals, new energy, and new materials.

Iambic Therapeutics is a cutting-edge AI-driven drug discovery platform that tackles the most challenging design problems in drug discovery, addressing unmet patient need. Its physics-based AI algorithms drive a high-throughput experimental platform, converting new molecular designs to new biological insights each week. Iambic's platform optimizes target product profiles, exploring multiple profiles in parallel to ensure that molecules are designed to solve the right problems in disease biology. It also optimizes drug candidates, deeply exploring chemical space to reveal novel mechanisms of action and deliver diverse high-quality leads.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

NoLabs is an open-source biolab that provides easy access to state-of-the-art models for bio research. It supports various tasks, including drug discovery, protein analysis, and small molecule design. NoLabs aims to accelerate bio research by making inference models accessible to everyone.

AlphaFold3 is an implementation of the Alpha Fold 3 model in PyTorch for accurate structure prediction of biomolecular interactions. It includes modules for genetic diffusion and full model examples for forward pass computations. The tool allows users to generate random pair and single representations, operate on atomic coordinates, and perform structure predictions based on input tensors. The implementation also provides functionalities for training and evaluating the model.

This repository contains the code for the paper Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. It demonstrates how finetuned LLMs can be used to generate stable materials, match or exceed the performance of domain specific models, mutate existing materials, and sample crystal structures conditioned on text descriptions. The method is distinct from CrystaLLM, which trains language models from scratch on CIF-formatted crystals.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

MD-Agent is a LLM-agent based toolset for Molecular Dynamics. It uses Langchain and a collection of tools to set up and execute molecular dynamics simulations, particularly in OpenMM. The tool assists in environment setup, installation, and usage by providing detailed steps. It also requires API keys for certain functionalities, such as OpenAI and paper-qa for literature searches. Contributions to the project are welcome, with a detailed Contributor's Guide available for interested individuals.

AIMNet2 Calculator is a package that integrates the AIMNet2 neural network potential into simulation workflows, providing fast and reliable energy, force, and property calculations for molecules with diverse elements. It excels at modeling various systems, offers flexible interfaces for popular simulation packages, and supports long-range interactions using DSF or Ewald summation Coulomb models. The tool is designed for accurate and versatile molecular simulations, suitable for large molecules and periodic calculations.

ADMET-AI is a platform for ADMET prediction using Chemprop-RDKit models trained on ADMET datasets from the Therapeutics Data Commons. It offers command line, Python API, and web server interfaces for making ADMET predictions on new molecules. The platform can be easily installed using pip and supports GPU acceleration. It also provides options for processing TDC data, plotting results, and hosting a web server. ADMET-AI is a machine learning platform for evaluating large-scale chemical libraries.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

AI2BMD is a program for efficiently simulating protein molecular dynamics with ab initio accuracy. The repository contains datasets, simulation programs, and public materials related to AI2BMD. It provides a Docker image for easy deployment and a standalone launcher program. Users can run simulations by downloading the launcher script and specifying simulation parameters. The repository also includes ready-to-use protein structures for testing. AI2BMD is designed for x86-64 GNU/Linux systems with recommended hardware specifications. The related research includes model architectures like ViSNet, Geoformer, and fine-grained force metrics for MLFF. Citation information and contact details for the AI2BMD Team are provided.

AIRS is a collection of open-source software tools, datasets, and benchmarks focused on Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. The goal is to develop and maintain an integrated, open, reproducible, and sustainable set of resources to advance the field of AI for Science. The current resources include tools for Quantum Mechanics, Density Functional Theory, Small Molecules, Protein Science, Materials Science, Molecular Interactions, and Partial Differential Equations.

ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It allows users to run benchmarking tasks on models with existing presets, offering predefined parameters and processing steps. The library facilitates benchmarking models on the entire suite, addressing challenges such as prompt structure, parsing, and scoring methods. Users can contribute to the project by following the developer notes.

GROMACS Copilot is an agent designed to automate molecular dynamics simulations for proteins in water using GROMACS. It handles system setup, simulation execution, and result analysis automatically, providing outputs such as RMSD, RMSF, Rg, and H-bonds. Users can interact with the agent through prompts and API keys from DeepSeek and OpenAI. The tool aims to simplify the process of running MD simulations, allowing users to focus on other tasks while it handles the technical aspects of the simulations.

A toolkit for computational chemistry research, featuring tools to facilitate automated workflows. Includes tools for NMR prediction, dynamic catalysis research, proton transfer analysis, amorphous oxides structure analysis, reweighting, and more. Users can install 'ai2-kit' via pip and explore various domain-specific and general tools for processing system data and filtering structures by model deviation.

Expert in generating and explaining mathematical, chemical, and computational formulas.

A digital homage to honor Stephen Wolfram's impact on computational science and technology and to celebrate his dedication to public education, powered by Stephen Wolfram's wealth of public presentations, writings, and live streams.

Assists in evaluating protein engineering tools.

💡 Inventor of languages

Skilled in Python, Linux, R, and more.

AI protein design assistant

Expert in Marian NMT models and training

大型语言模型的学习和训练指导专家

Formal OpenMC Python API and debugging expert.

Blends academic insights & accessible explanations on P vs NP, drawing from Lance Fortnow's works.

A playful CFD advisor

Expert in OpenFOAM for CFD queries

Expert in GH-Python scripting & Grasshopper components

Advises on Bioconda recipes, following community guidelines

A interactive chatbot

Advanced AI for complex computations and data analysis.