Best AI tools for< Molecular Design >

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.

HUAWEI Cloud Pangu is an AI tool designed for accelerating drug discovery by optimizing drug molecules. It offers features such as Molecule Search, Molecule Optimizer, and Pocket Molecule Design. Users can submit molecules for optimization and view historical optimization results. The tool is based on the MindSpore framework and has been visited over 300,000 times since August 23, 2021.

Allchemy is a resource-aware AI platform for drug discovery. It combines state-of-the-art computational synthesis with AI algorithms to predict molecular properties. Within minutes, Allchemy creates thousands of synthesizable lead candidates meeting user-defined profiles of drug-likeness, affinity towards specific proteins, toxicity, and a range of other physical-chemical measures. Allchemy encompasses the entire resource-to-drug design process and has been used in academic, corporate and classified environments worldwide to: Design synthesizable leads targeting specific proteins Evolve scaffolds similar to desired drugs Design “circular” drug syntheses from renewable materials Interface with and instruct automated synthesis platforms and optimize pilot-scale processes Operate “iterative synthesis” schemes Predict side reactions and create forensic “synthetic signatures” of hazardous/toxic molecules Design synthetic degradation and recovery cycles for various types of feedstocks and functional target molecules

VantAI is an AI application focused on generative AI-enabled drug discovery. Their mission is to unlock a new chapter in medicine by making protein interactions programmable. They have an integrated discovery platform with phase-shifting technologies designed to unlock the full potential of the proximity modulator modality. VantAI collaborates with industry leaders to build the future of therapeutic design. The company has launched Neo-1, the first AI model to rewire molecular interactions by unifying structure prediction and generation.

Deep Genomics is a company that uses artificial intelligence (AI) to develop RNA therapies for genetic diseases. The company's AI platform is designed to identify novel targets and evaluate thousands of possibilities to identify the best therapeutic candidates. Deep Genomics is currently developing BigRNA+, which will expand the number of mechanisms and genetic variants the company can pursue.

Genesis Therapeutics is a cutting-edge platform that leverages molecular AI technology to discover and develop highly potent and selective medicines. Their proprietary AI platform, GEMS, combines AI and physics research to target challenging protein structures and create innovative drug candidates with exceptional efficacy. The company's success is driven by a collaborative approach, bringing together experts in AI and biotech to tackle complex drug discovery challenges.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

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.

Chemprop is a PyTorch-based framework for training and evaluating message-passing neural networks (MPNNs) for molecular property prediction. Originally developed for research purposes, Chemprop offers a comprehensive set of tools and features for training models and analyzing molecular representations. The package underwent a recent major release (v2.0.0) with significant improvements and updates.

Tempus is an AI-enabled precision medicine company that brings the power of data and artificial intelligence to healthcare. With the power of AI, Tempus accelerates the discovery of novel targets, predicts the effectiveness of treatments, identifies potentially life-saving clinical trials, and diagnoses multiple diseases earlier. Tempus's innovative technology includes ONE, an AI-enabled clinical assistant; NEXT, a tool to identify and close gaps in care; LENS, a platform to find, access, and analyze multimodal real-world data; and ALGOS, algorithmic models connected to Tempus's assays to provide additional insight.

Tempus is an AI-enabled precision medicine company that brings the power of data and artificial intelligence to healthcare. With the power of AI, Tempus accelerates the discovery of novel targets, predicts the effectiveness of treatments, identifies potentially life-saving clinical trials, and diagnoses multiple diseases earlier. Tempus' innovative technology includes ONE, an AI-enabled clinical assistant; NEXT, which identifies and closes gaps in care; LENS, which finds, accesses, and analyzes multimodal real-world data; and ALGOS, algorithmic models connected to Tempus' assays to provide additional insight.

Paige is a leading AI company revolutionizing pathology with next-generation technology. They provide diagnostic and biomarker AI, predictive analytics technology, and AI-assisted applications to support cancer detection, subtyping, and molecular biomarker discovery from tissue samples. Paige offers a range of AI suites for prostate, breast, colon, and PanCancer, as well as the innovative Paige Alba™ multi-modal co-pilot. Their advanced AI technology and services help streamline AI development, optimize existing applications, and drive groundbreaking advancements in cancer care.

Lavo Life Sciences is an AI-accelerated crystal structure prediction application that helps in drug development by providing accurate predictions for small molecule drugs. The application utilizes AI technology to optimize solid-state formulations, reduce turnaround time, mitigate risks, and discover novel polymorphs, ultimately streamlining the pharmaceutical research and development process.

Atomwise is an artificial intelligence (AI)-driven drug discovery company that uses machine learning to discover and develop new small molecule medicines. The company's AI engine combines the power of convolutional neural networks with massive chemical libraries to identify new drug candidates. Atomwise has a wholly owned pipeline of drug discovery programs and also partners with other pharmaceutical companies to co-develop drugs. The company's investors include prominent venture capital firms and pharmaceutical companies.

Atomwise is an AI-powered drug discovery company that uses machine learning to identify new small molecule medicines. The company's platform combines the power of convolutional neural networks with massive chemical libraries to discover new drug candidates. Atomwise has a portfolio of wholly owned and co-developed pipeline assets, and is backed by prominent investors.

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

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.

New AI Drug Discovery is a repository focused on the applications of Large Language Models (LLM) in drug discovery. It provides resources, tools, and examples for leveraging LLM technology in the pharmaceutical industry. The repository aims to showcase the potential of using AI-driven approaches to accelerate the drug discovery process, improve target identification, and optimize molecular design. By exploring the intersection of artificial intelligence and drug development, this repository offers insights into the latest advancements in computational biology and cheminformatics.

Awesome Generative AI is a curated list of modern Generative Artificial Intelligence projects and services. Generative AI technology creates original content like images, sounds, and texts using machine learning algorithms trained on large data sets. It can produce unique and realistic outputs such as photorealistic images, digital art, music, and writing. The repo covers a wide range of applications in art, entertainment, marketing, academia, and computer science.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

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.

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.

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.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

Towhee is a cutting-edge framework designed to streamline the processing of unstructured data through the use of Large Language Model (LLM) based pipeline orchestration. It can extract insights from diverse data types like text, images, audio, and video files using generative AI and deep learning models. Towhee offers rich operators, prebuilt ETL pipelines, and a high-performance backend for efficient data processing. With a Pythonic API, users can build custom data processing pipelines easily. Towhee is suitable for tasks like sentence embedding, image embedding, video deduplication, question answering with documents, and cross-modal retrieval based on CLIP.

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.

AITom is an open-source platform for AI-driven cellular electron cryo-tomography analysis. It is developed to process large amounts of Cryo-ET data, reconstruct, detect, classify, recover, and spatially model different cellular components using state-of-the-art machine learning approaches. The platform aims to automate cellular structure discovery and provide new insights into molecular biology and medical applications.

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

Docling is a tool that bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. It can convert any PDF document to JSON or Markdown format, understand detailed page layout, reading order, recover table structures, extract metadata such as title, authors, references, and language, and optionally apply OCR for scanned PDFs. The tool is designed to be stable, lightning fast, and suitable for macOS and Linux environments.

AI-Drug-Discovery-Design is a repository focused on Artificial Intelligence-assisted Drug Discovery and Design. It explores the use of AI technology to accelerate and optimize the drug development process. The advantages of AI in drug design include speeding up research cycles, improving accuracy through data-driven models, reducing costs by minimizing experimental redundancies, and enabling personalized drug design for specific patients or disease characteristics.

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.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

This repository is dedicated to collecting and categorizing papers related to Retrieval-Augmented Generation (RAG) for AI-generated content. It serves as a survey repository based on the paper 'Retrieval-Augmented Generation for AI-Generated Content: A Survey'. The repository is continuously updated to keep up with the rapid growth in the field of RAG.

Research assistant for Atomic, Molecular, and Optics Physics

Assists in evaluating protein engineering tools.

A GPT that can provides lectures on Statistical Mechanics

I assist with statistical analysis for molecular biology

Academic-level genomics expertise.

Genome Guide: Powered by OpenAI - A leader in genomic data analysis.

Echoes Watson & Crick, educates on DNA with depth.

Professional chemistry assistant, SMILES/SMART supported molecule and reaction diagrams, and more!

A guide to electron configurations with a fun, cartoony approach.

生物化学领域的知识型助手，避免提供医学建议。

Script Examples and Resource Center for Helping with LAMMPS Input Generation and High-quality Tutorials (SERCHLIGHT)

A Beginners Guide to Quantum Magic