Best AI tools for< Medicinal 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.

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.

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.

Jotlify is an AI-powered platform that simplifies complex research papers, making them accessible and easy to understand for students, researchers, professionals, and curious minds. It transforms dense academic content into engaging stories and insights, bridging the gap between complex research and easy understanding. With Jotlify, users can uncover stories and insights that can transform their understanding and impact various aspects of their lives.

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.

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.

The Institute for Protein Design is a research institute at the University of Washington that uses computational design to create new proteins that solve modern challenges in medicine, technology, and sustainability. The institute's research focuses on developing new protein therapeutics, vaccines, drug delivery systems, biological devices, self-assembling nanomaterials, and bioactive peptides. The institute also has a strong commitment to responsible AI development and has developed a set of principles to guide its use of AI in research.

Quiz Wizard is an AI-powered tool that helps teachers and educators create quizzes, flashcards, and theoretical sheets on any topic in seconds. With Quiz Wizard, you can save time and provide tailored educational content for your students. Whether you're teaching physics, chemistry, languages, medicine, or any other subject, Quiz Wizard has got you covered.

Seamless is an AI Literature Review Tool for Scientific Research that enables users to draft literature reviews 100x faster by leveraging advanced AI technology. It allows researchers to find relevant papers and create a draft directly from an excerpt of their work. Seamless utilizes the Semantic Scholar database of scientific papers and large language models like GPT-4 to generate literature reviews in various fields such as engineering, computer science, chemistry, biology, law, medicine, pharma, and business. The tool is designed to streamline the process of literature review creation and enhance the efficiency of researchers and students.

The website is a comprehensive platform providing medical news, articles, and resources covering a wide range of health topics such as COVID-19, artificial intelligence in healthcare, diseases, treatments, and medical advancements. It offers insights from experts, interviews, white papers, and newsletters in the fields of medicine and life sciences. Users can access information on various health categories, research findings, safety summaries, and trending stories in the medical and life sciences domains.

Sunoh is a medical AI scribe that uses ambient listening technology to convert natural conversations between healthcare providers and patients into clinical documentation. It offers a unique and immersive experience for both doctors and patients, making the documentation of clinical notes faster and more efficient than ever before. Sunoh can be used with your EHR to accelerate your documentation.

Medical Chat is an advanced AI assistant designed for healthcare professionals, providing instant and accurate medical answers for both human and veterinary medicine. Its capabilities include diagnosing medical conditions, generating differential diagnosis reports, creating patient-specific clinic plans, and offering comprehensive drug information. Medical Chat utilizes the latest LLM models, including ChatGPT 3.5 and 4.0, to deliver reliable and up-to-date medical knowledge. The platform also features a vast database of professional medical textbooks, veterinary books, and PubMed articles, ensuring evidence-based responses. With its HIPAA compliance and commitment to data privacy, Medical Chat empowers healthcare providers to enhance their diagnostic capabilities and improve patient outcomes.

Medical Brain is an AI-powered clinical assistant designed for both patients and providers. It engages with users to identify health risks and care gaps early, providing actionable insights and guidance to improve outcomes and intercept high-cost ER visits. The platform monitors patients 24/7, aggregates and understands all patient data, and generates real-time actions based on AI clinical decision support and automation. Medical Brain incorporates evidence-based best practices in various clinical modules and continuously learns from user experiences to enhance efficiency and intelligence.

The Medical Futurist is a digital health and AI-focused media platform that provides insights, research, and educational resources to healthcare professionals and industry leaders. It covers topics such as artificial intelligence in medicine, the future of pharma, and emerging trends in digital health. The platform also offers keynote speeches, courses, and e-books on these topics.

Subtle Medical develops vendor-neutral software solutions that improve image quality on regular and accelerated image protocols, allowing radiologists to expedite patient care. Their AI solutions for MRI and PET reduce image noise and increase image sharpness, leading to improved diagnostic confidence and a better patient experience. Subtle Medical's software seamlessly integrates with all scanners and clients, supporting both cloud and on-prem deployment. It processes images within seconds, fitting seamlessly into existing workflows.

Oncora Medical is a healthcare technology company that provides software and data solutions to oncologists and cancer centers. Their products are designed to improve patient care, reduce clinician burnout, and accelerate clinical discoveries. Oncora's flagship product, Oncora Patient Care, is a modern, intelligent user interface for oncologists that simplifies workflow, reduces documentation burden, and optimizes treatment decision making. Oncora Analytics is an adaptive visual and backend software platform for regulatory-grade real world data analytics. Oncora Registry is a platform to capture and report quality data, treatment data, and outcomes data in the oncology space.

Heroku is a cloud platform that lets companies build, deliver, monitor, and scale apps. It simplifies the process of deploying applications by providing a platform as a service (PaaS) solution. With Heroku, developers can focus on writing code without worrying about infrastructure management. The platform supports multiple programming languages and frameworks, making it versatile for various types of applications.

LITFL (Life in the Fast Lane) is a website that provides free medical education resources, including articles, videos, podcasts, and infographics. The site is run by a team of doctors and other healthcare professionals who are passionate about sharing their knowledge and expertise with the world. LITFL has been featured in numerous publications, including The New York Times, The Guardian, and The Wall Street Journal.

Lyrebird Health is an AI-powered medical scribe that automates documentation tasks for healthcare providers. It uses natural language processing (NLP) to listen in on patient encounters and generate accurate, medico-legally compliant notes, letters, and assessments. Lyrebird Health is designed to save clinicians time and reduce burnout, allowing them to focus on providing better care to their patients.

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.

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 does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

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.

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.

This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

This repository maintains a list of research papers on LLM-based Multi-Agents, categorized into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. The repository also includes a survey paper on LLM-based Multi-Agents and a table summarizing the key findings of the survey.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

The Octopus-v4 project aims to build the world's largest graph of language models, integrating specialized models and training Octopus models to connect nodes efficiently. The project focuses on identifying, training, and connecting specialized models. The repository includes scripts for running the Octopus v4 model, methods for managing the graph, training code for specialized models, and inference code. Environment setup instructions are provided for Linux with NVIDIA GPU. The Octopus v4 model helps users find suitable models for tasks and reformats queries for effective processing. The project leverages Language Large Models for various domains and provides benchmark results. Users are encouraged to train and add specialized models following recommended procedures.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Awesome-Colorful-LLM is a meticulously assembled anthology of vibrant multimodal research focusing on advancements propelled by large language models (LLMs) in domains such as Vision, Audio, Agent, Robotics, and Fundamental Sciences like Mathematics. The repository contains curated collections of works, datasets, benchmarks, projects, and tools related to LLMs and multimodal learning. It serves as a comprehensive resource for researchers and practitioners interested in exploring the intersection of language models and various modalities for tasks like image understanding, video pretraining, 3D modeling, document understanding, audio analysis, agent learning, robotic applications, and mathematical research.

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

Find and summarize recent papers in biology, chemistry, and biomedical sciences.

I provide knowledge on medicinal plants and brain development, with a humorous and educational touch!

A simulation game where players learn grow medicinal plants, craft remedies, and manage a herbal healing garden. Another AI Tiny Game by Dave Lalande

Adaptable, comprehensive guide in medicinal herbs, balancing detail and accessibility.

Focused on groundbreaking plant biology research for agricultural, medicinal, and environmental advancements.

Herbalism expert offering natural remedy guidance.

Especialista em teses e dissertações de Medicina Veterinária no Brasil.

Líder en Latinoamérica en Criminalística, Criminología, Medicina Legal y Forense, e Investigación Criminal

Deciphers medical news, explaining complex terms in simple English and Japanese

Expert in medical imaging AI, adept in machine learning tools.

Helps explain medical consents

Friendly, conversational tone for medical acronyms.

Expert in NFPA 99-2018 for medical gas system compliance and guidance.

Expert in Medical Supplies and Medicare Guidelines

Guide on medical equipment, ailment-based recommendations & image analysis

A guide for aspiring med students, aiding in interviews, MCAT prep, and application advice.

Expert Medical Technologist offering tailored consultations

Informative guide on medical devices and healthcare technology.

A trial version of AVA 2.0, the World's first AI Interview Platform for Medical School Interview Interviews - https://ai.theaspiringmedics.co.uk/

Describe your medical signs and symptoms. Optionally also list any applicable known lab test results. Further lab tests will be recommended. Any web searches may be requested explicitly. Extra tests by these providers may also be requested explicitly: QuestHealth, WalkInLab, RadiologyAssist