Best AI tools for< Drug Discovery Scientist >

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.

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.

Insitro is a drug discovery and development company that uses machine learning and data to identify and develop new medicines. The company's platform integrates in vitro cellular data produced in its labs with human clinical data to help redefine disease. Insitro's pipeline includes wholly-owned and partnered therapeutic programs in metabolism, oncology, and neuroscience.

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.

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.

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.

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

Ignota Labs is a technology company focused on rescuing failing drugs and bringing new life to abandoned projects, ultimately providing hope to patients. The company utilizes a proprietary AI model, SAFEPATH, which applies deep learning to bioinformatics and cheminformatics datasets to solve drug safety issues. Ignota Labs aims to identify promising drug targets, address safety problems in clinical trials, and accelerate the delivery of therapeutically effective drugs to patients.

Kuano is an AI tool that focuses on redefining drug discovery using Quantum and AI technologies. The platform offers world-class scientific expertise in quantum physics, AI, and medicinal chemistry to revolutionize the drug design process. Kuano aims to leverage cutting-edge technologies to accelerate the discovery of new drugs and improve healthcare outcomes.

Exscientia is a technology-driven drug design and development company that combines precision design with integrated experimentation to create more effective medicines for patients faster. They operate at the interfaces of human ingenuity, artificial intelligence (AI), automation, and physical engineering, pioneering the use of AI in drug discovery. Exscientia aims to change the underlying economics of drug discovery by rapidly advancing the best scientific ideas into medicines for patients.

BenevolentAI is a leader in applying advanced AI to accelerate biopharma drug discovery blending science and technology with a focus on finding solutions for complex diseases. We empower both biopharmaceutical companies and our internal scientists to harness the full potential of data and AI to accelerate the next generation of scientific advances. We have built our AI-enabled drug discovery engine to drive a revolution in drug discovery. The Benevolent Platform™ unlocks the power of a vast biomedical data landscape to provide a multidimensional representation of human biology across all diseases. We believe this approach will improve the probability of clinical success, and help us deliver life-changing treatments to patients – because it matters.

JADBio is an automated machine learning (AutoML) platform designed to accelerate biomarker discovery and drug development processes. It offers a no-code solution that automates the discovery of biomarkers and interprets their role based on research needs. JADBio can parse multi-omics data, including genomics, transcriptome, metagenome, proteome, metabolome, phenotype/clinical data, and images, enabling users to efficiently discover valuable insights. The platform is purpose-built for various conditions such as cancer, immune, endocrine, metabolic system, chronic diseases, aging, infectious diseases, and mental health, offering solutions for early biomarker discovery, drug repurposing, lead identification, compound optimization, trial monitoring, and response to treatment. JADBio is trusted by partners in precision health & medicine and is continuously evolving to disrupt drug discovery times and costs at all stages.

Genesis Therapeutics is a cutting-edge platform that leverages molecular AI technology to discover and develop highly potent and selective medicines. Their proprietary Generative AI for Drug Discovery (GEMS) platform combines AI and physics research to identify drug candidates against challenging targets with unprecedented speed and accuracy. The company's innovative approach, powered by collaborative minds across AI and biotech, is revolutionizing the drug discovery process.

Valo is a company that uses AI-driven technology to transform the discovery and development of life-changing medicines. They combine machine learning, tissue biology, and patient data to create a suite of powerful capabilities that bring the future of drug discovery and development to bear. Valo's team of software engineers, data scientists, biologists, medicinal chemists, and big-picture thinkers are dedicated to advancing the combined power of technology and patient data.

neurons.bio is an AI application that offers a unique collection of over 100 AI agents designed for drug development, medicine, and life science research. These agents perform specific tasks efficiently, retrieve data from various sources, and provide insights to accelerate research processes. The platform aims to revolutionize drug discovery and development by integrating cutting-edge LLM technology with domain-specific agents, reducing research costs and time to clinic.

NVIDIA is a world leader in artificial intelligence computing. The company's products and services are used by businesses and governments around the world to develop and deploy AI applications. NVIDIA's AI platform includes hardware, software, and tools that make it easy to build and train AI models. The company also offers a range of cloud-based AI services that make it easy to deploy and manage AI applications. NVIDIA's AI platform is used in a wide variety of industries, including healthcare, manufacturing, retail, and transportation. The company's AI technology is helping to improve the efficiency and accuracy of a wide range of tasks, from medical diagnosis to product design.

Recursion is a techbio company that uses artificial intelligence to accelerate drug discovery. The company's platform combines hardware, software, and data to create a more efficient and effective drug discovery process. Recursion has a broad pipeline of drug candidates in development, and it has partnered with several leading pharmaceutical companies. The company is headquartered in Salt Lake City, Utah.

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.

SOMA is a Research Automation Platform that accelerates medical innovation by providing up to 100x speedup through process automation. The platform analyzes medical research articles, extracts important concepts, and identifies causal and associative relationships between them. It organizes this information into a specialized database forming a knowledge graph. Researchers can retrieve causal chains, access specific research articles, and perform tasks like concept analysis, drug repurposing, and target discovery. SOMA enhances literature review efficiency by finding relevant articles based on causal chains and keywords specified by the user. It empowers researchers to focus on their research by saving up to 95% of the time spent on pre-processing documents. The platform offers freemium access with extended functionality for 14 days and advanced features available through subscription.

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.

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to infectious and neglected disease research. It offers an open-source, low-code solution that provides seamless access to AI/ML models for drug discovery. Models housed in the hub come from two sources: published models from literature (with due third-party acknowledgment) and custom models developed by the Ersilia team or contributors.

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.

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.

Generative-AI-Drug-Discovery is a public repository on GitHub focused on using tensor network machine learning approaches to accelerate GenAI for drug discovery. The repository aims to implement effective architectures and methodologies into Large Language Models (LLMs) to enhance Drug Discovery Generative AI performance.

Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery. This library is a Python client to interact with the Polaris Hub. It allows you to download Polaris datasets and benchmarks, evaluate a custom method against a Polaris benchmark, and create and upload new datasets and benchmarks.

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.

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.

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.

Zeta is a tool designed to build state-of-the-art AI models faster by providing modular, high-performance, and scalable building blocks. It addresses the common issues faced while working with neural nets, such as chaotic codebases, lack of modularity, and low performance modules. Zeta emphasizes usability, modularity, and performance, and is currently used in hundreds of models across various GitHub repositories. It enables users to prototype, train, optimize, and deploy the latest SOTA neural nets into production. The tool offers various modules like FlashAttention, SwiGLUStacked, RelativePositionBias, FeedForward, BitLinear, PalmE, Unet, VisionEmbeddings, niva, FusedDenseGELUDense, FusedDropoutLayerNorm, MambaBlock, Film, hyper_optimize, DPO, and ZetaCloud for different tasks in AI model development.

This repository is a collection of papers on leveraging Large Language Models (LLMs) in Graph Tasks. It provides a comprehensive overview of how LLMs can enhance graph-related tasks by combining them with traditional Graph Neural Networks (GNNs). The integration of LLMs with GNNs allows for capturing both structural and contextual aspects of nodes in graph data, leading to more powerful graph learning. The repository includes summaries of various models that leverage LLMs to assist in graph-related tasks, along with links to papers and code repositories for further exploration.

A collection of papers and resources about Large Language Models (LLM) for Graph Learning (Graph). Integrating LLMs with graph learning techniques to enhance performance in graph learning tasks. Categorizes approaches based on four primary paradigms and nine secondary-level categories. Valuable for research or practice in self-supervised learning for recommendation systems.

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.

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.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

This repository serves as an educational resource for pharmaceutical and chemistry students to learn the basics of Deep Learning through a collection of Jupyter Notebooks. The content covers various topics such as Introduction to Jupyter, Python, Cheminformatics & RDKit, Linear Regression, Data Science, Linear Algebra, Neural Networks, PyTorch, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks, Autoencoders, Graph Neural Networks, and Summary. The notebooks aim to provide theoretical concepts to understand neural networks through code completion, but instructors are encouraged to supplement with their own lectures. The work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Awesome OpenVINO is a curated list of AI projects based on the OpenVINO toolkit, offering a rich assortment of projects, libraries, and tutorials covering various topics like model optimization, deployment, and real-world applications across industries. It serves as a valuable resource continuously updated to maximize the potential of OpenVINO in projects, featuring projects like Stable Diffusion web UI, Visioncom, FastSD CPU, OpenVINO AI Plugins for GIMP, and more.

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.

A Specialized Biochemistry and Pharmaceutical Research Assistant

AI protein design assistant

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Non-judgemental drug harm reduction assistant providing safe usage and interaction info.

An expert in Drug Delivery Systems Industry, providing in-depth, accurate insights.

A drug encyclopedia for medical professionals, providing detailed drug information and tailored suggestions.

GPT for drug interactions. Enter at least two medication names to learn about potential drug interactions.

Remember Drug Wars on the TI-83?

Medication expert assisting with drug information

Comprehensive drug and medication informations.

Adivce on all things drugs!

Expert in pharmacotherapy treatment.

薬剤師ナカショウが薬や健康知識について山口弁で答えるBotです。※あくまで参考程度にお使いください。

Assiste en français pour évaluer les médicaments avec des recherches en ligne.

Expert in pharmaceuticals, providing informative insights.

A tool for designing nanocarrier systems, tailored to drugs and patient profiles.

To provide a comprehensive AI-assisted learning experience for 2nd-year pharmacy students, aiming to enhance understanding, retention, and application of pharmaceutical knowledge.

Mafya dedesi

Especialista em Teses e Dissertações de Farmácia

User-Friendly Pharma Marketing Guide. Help answer questions, and provide ideas on targeting consumers and HCPs