Best AI tools for< Drug Development Analyst >

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.

AiCure provides a patient-centric eClinical trial management platform that enhances drug development through improved medication adherence rates, more powerful analysis and prediction of treatment response using digital biomarkers, and reduced clinical tech burden. AiCure's solutions support traditional, decentralized, or hybrid trials and offer flexibility to meet the needs of various research designs.

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 insights. The platform is tailored for various conditions such as cancer, immune system disorders, chronic diseases, infectious diseases, and mental health, providing solutions for biomarker discovery, drug repurposing, lead identification, and treatment response assessment.

Biofourmis is a healthcare technology company that provides a connected technology platform for care delivery and drug development. The platform enables healthcare systems and pharmaceutical companies to deliver care and conduct clinical trials remotely, and to collect and analyze data to improve patient outcomes. Biofourmis's solutions have been shown to improve clinical, operational, and economic outcomes, such as reducing 30-day readmissions by 70%, detecting deterioration 21 hours sooner, and reducing cost of care by up to 38%.

IXICO is a precision analytics company specializing in intelligent insights in neuroscience. They offer a range of services for drug development analytics, imaging operations, and post-marketing consultancy. With a focus on technology and innovation, IXICO provides expertise in imaging biomarkers, radiological reads, volumetric MRI, PET & SPECT, and advanced MRI. Their TrialTracker platform and Assessa tool utilize innovation and AI for disease modeling and analysis. IXICO supports biopharmaceutical companies in CNS clinical research with cutting-edge neuroimaging techniques and AI technology.

Dang.ai is an AI Tools Directory that provides a comprehensive list of AI tools and services. It offers a platform for users to discover and explore various AI-powered applications across different categories such as image design, writing, business, chat, audio, chatbot, art, productivity, video, TTS, marketing, code search, and more. Users can find tools for automating emails, marketing strategies, content optimization, video summarization, writing enhancement, anime art generation, web accessibility, survey platforms, sketch rendering, content generation, highlight finding, web accessibility, digital commerce insights, study tools, photo editing, drug development, media creation, logo design, and much more.

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.

CCN is a website providing news, analysis, and guides related to cryptocurrencies, blockchain technology, and AI developments. The platform covers a wide range of topics including crypto investing, exchanges, gambling, technology advancements, and regulatory updates. With a focus on delivering accurate and up-to-date information, CCN aims to educate and inform its audience about the latest trends and developments in the crypto and AI industries.

Intelligencia AI is a leading provider of AI-powered solutions for the pharmaceutical industry. Our suite of solutions helps de-risk and enhance clinical development and decision-making. We use a combination of data, AI, and machine learning to provide insights into the probability of success for drugs across multiple therapeutic areas. Our solutions are used by many of the top global pharmaceutical companies to improve their R&D productivity and make more informed decisions.

Lavo Life Sciences is an AI-accelerated crystal structure prediction application that helps in drug development by providing accurate predictions. The platform offers solutions to de-risk pipelines, optimize solid-state formulations, and avoid late-stage surprises using AI technology. Lavo Life Sciences combines the expertise of chemists and engineers in AI and computational chemistry to deliver innovative solutions for drug development teams. The application aims to reduce turnaround time for crystal form identification, minimize risks of unexpected crystal forms, optimize drug formulations, and discover novel polymorphs with improved properties.

Genesis Therapeutics is an AI-powered drug discovery platform that leverages advanced molecular AI technology to unlock novel protein targets, explore untapped chemical space, and accelerate the development of critical new medicines. The company comprises a team of drug hunters, deep learning researchers, and software engineers working together to drive AI in biochemistry and discover new therapies for patients with severe disorders. Their pioneering approach combines 3D spatial graph modeling and cutting-edge molecular simulation to generate innovative drug candidates.

Unlearn.ai is an AI-powered digital twins solution provider that optimizes clinical trials. Their TwinRCTs enable confident and quick clinical trials in various medical fields such as neuroscience, immunology, metabolic disease, and more. By utilizing digital twins, Unlearn.ai aims to revolutionize the traditional clinical trial process by reducing the time to enrollment, improving decision-making in early-stage studies, and attracting study participants. Their innovative approach combines artificial intelligence with patient-level data to enhance the efficiency and effectiveness of clinical research.

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.

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.

BioXcel Therapeutics, Inc. is a clinical-stage biopharmaceutical company developing transformative medicines in neuroscience and immuno-oncology utilizing artificial intelligence, or AI, techniques. The company's proprietary AI platform is used to identify, re-innovate, and develop potential new therapies. BioXcel Therapeutics has a pipeline of product candidates in various stages of development, including BXCL501 for agitation in dementia, BXCL701 for cocaine use disorder, and BXCL801 for acute suicidal ideation and behavior in patients with major depressive disorder.

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.

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.

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.

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.

DrugCard is an AI-enabled Data Intelligence platform designed to drive Pharmacovigilance forward by streamlining drug safety routines. It offers solutions for Continuous and regular, Traceable and transparent, Accurate, complete, holistic, and Scalable drug safety processes. The platform supports over 100 languages, monitors 970+ local journals, and saves up to 70% time compared to manual approaches. DrugCard caters to CROs, MAHs, and freelancers in the pharmaceutical industry, providing efficient local literature screening services.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

This repository is a curated list of research papers that explore the integration of Large Language Model (LLM) technology with tabular data. It aims to provide a comprehensive resource for researchers and practitioners interested in this emerging field. The repository includes papers on a wide range of topics, including table-to-text generation, table question answering, and tabular data classification. It also includes a section on related datasets and resources.

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 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

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.

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.

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.

Open Life Science AI: Medical Reasoning Tasks is a collaborative hub for developing cutting-edge reasoning tasks for Large Language Models (LLMs) in the medical, healthcare, and clinical domains. The repository aims to advance AI capabilities in healthcare by fostering accurate diagnoses, personalized treatments, and improved patient outcomes. It offers a diverse range of medical reasoning challenges such as Diagnostic Reasoning, Treatment Planning, Medical Image Analysis, Clinical Data Interpretation, Patient History Analysis, Ethical Decision Making, Medical Literature Comprehension, and Drug Interaction Assessment. Contributors can join the community of healthcare professionals, AI researchers, and enthusiasts to contribute to the repository by creating new tasks or improvements following the provided guidelines. The repository also provides resources including a task list, evaluation metrics, medical AI papers, and healthcare datasets for training and evaluation.

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.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

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.

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.

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.

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.

LinkedIn_AIHawk is an automated tool designed to revolutionize the job search and application process on LinkedIn. It leverages automation and artificial intelligence to efficiently apply to relevant positions, personalize responses, manage application volume, filter listings, generate dynamic resumes, and handle sensitive information securely. The tool aims to save time, increase application relevance, and enhance job search effectiveness in today's competitive landscape.

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.

🔬💊 Navigate the high-risk, high-reward world of biotech and pharma investing! Discover breakthrough therapies 🧬📈, understand drug development 🧪📊, and evaluate investment opportunities 🚀💰. Invest wisely in innovation! 💡🌐 Not a financial advisor. 🚫💼

Expert and respectful guide on aging research and its societal impacts.

Comprehensive Guide in Clinical Trials & Regulations

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.

A Specialized Biochemistry and Pharmaceutical Research Assistant

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

AI protein design assistant