Best AI tools for< Chemical Synthesis >

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.

C&EN, a publication of the American Chemical Society, provides the latest news and insights on the chemical industry, including research, technology, business, and policy. It covers a wide range of topics, including analytical chemistry, biological chemistry, business, careers, education, energy, environment, food, materials, people, pharmaceuticals, physical chemistry, policy, research integrity, safety, and synthesis.

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

Osium AI is a cutting-edge AI-powered software designed to accelerate the development of sustainable and high-performance materials and chemicals. The platform leverages proprietary technology developed by experts with 10 years of experience in AI and authors of multiple AI patents. Osium AI offers a comprehensive solution that covers every step of materials and chemicals development cycles, from formulation and characterization to scale-up and manufacturing. The software is flexible, adaptable to various R&D projects, and eliminates trial-and-error approaches, unlocking the full potential of R&D with its advanced functionalities.

Math Sniper is an AI-powered application designed to provide precise math solutions, exam preparation assistance, and exploration of mathematical concepts. The app offers step-by-step solutions to math challenges at all levels, connects users with math tutors for personalized help, and covers a wide range of subjects beyond mathematics, such as biology, chemistry, physics, history, economics, and language tasks. With features like Snap & Ask for instant answers, step-by-step explanations, and a user-friendly interface, Math Sniper aims to enhance users' understanding of complex concepts and facilitate learning in various disciplines.

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.

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.

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.

SpoiledChild is a skincare and haircare brand that uses AI to personalize product recommendations for its customers. The company's products are designed to help people look and feel younger, and they are made with high-quality ingredients that are free of harsh chemicals. SpoiledChild offers a wide range of products, including serums, moisturizers, masks, and supplements. The company also has a team of experts who can provide personalized advice on how to use their products. SpoiledChild is committed to sustainability, and they use recycled materials in their packaging and offer a refill program for their products.

C3 AI provides a comprehensive Enterprise AI application development platform and a large and growing family of turnkey enterprise AI applications. C3 AI's platform provides all necessary software services in one integrated suite to rapidly develop, provision, and operate Enterprise AI applications. C3 AI's applications are designed to meet the business-critical needs of global enterprises in various industries, including manufacturing, financial services, government, utilities, oil and gas, chemicals, agribusiness, defense and intelligence.

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.

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.

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.

This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.

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.

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 '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.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and follows a process of embedding docs and queries, searching for top passages, creating summaries, scoring and selecting relevant summaries, putting summaries into prompt, and generating answers. Users can customize prompts and use various models for embeddings and LLMs. The tool can be used asynchronously and supports adding documents from paths, files, or URLs.

llm-swarm is a tool designed to manage scalable open LLM inference endpoints in Slurm clusters. It allows users to generate synthetic datasets for pretraining or fine-tuning using local LLMs or Inference Endpoints on the Hugging Face Hub. The tool integrates with huggingface/text-generation-inference and vLLM to generate text at scale. It manages inference endpoint lifetime by automatically spinning up instances via `sbatch`, checking if they are created or connected, performing the generation job, and auto-terminating the inference endpoints to prevent idling. Additionally, it provides load balancing between multiple endpoints using a simple nginx docker for scalability. Users can create slurm files based on default configurations and inspect logs for further analysis. For users without a Slurm cluster, hosted inference endpoints are available for testing with usage limits based on registration status.

LlamaBot is a Pythonic bot interface to Large Language Models (LLMs), providing an easy way to experiment with LLMs in Jupyter notebooks and build Python apps utilizing LLMs. It supports all models available in LiteLLM. Users can access LLMs either through local models with Ollama or by using API providers like OpenAI and Mistral. LlamaBot offers different bot interfaces like SimpleBot, ChatBot, QueryBot, and ImageBot for various tasks such as rephrasing text, maintaining chat history, querying documents, and generating images. The tool also includes CLI demos showcasing its capabilities and supports contributions for new features and bug reports from the community.

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.

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. For more architecture details, see Milvus Architecture Overview. Milvus was released under the open-source Apache License 2.0 in October 2019. It is currently a graduate project under LF AI & Data Foundation.

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.

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

A scientific guide to chemicals and their properties.

Your chemical engineering expert.

Chemical biology expert for literature, data viz, and coding

Turbocharge your research and streamline your path to breakthrough findings. Leveraging the vast resources of PubChem, this GPT taps into a wealth of chemical data—from substances to proteins and patents—unleashing the full potential of your data for richer, more informed discoveries.

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

Expert on lye, providing detailed chemical information and safety guidelines.

Dietician that helps those with IBS manage their symptoms via FODMAPs. FODMAP stands for fermentable oligosaccharides, disaccharides, monosaccharides and polyols. These are the chemical names of 5 naturally occurring sugars that are not well absorbed by your small intestine.

Assistente para Engenharia Química e Teses

Advanced AI for chemistry, offering innovative solutions, process optimizations, and safety assessments, powered by OpenAI.

Guides the implementation of fluid mechanics principles in engineering projects.

Explores the science of indestructible materials and concepts.

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

Identifies signals in 1H and 13C NMR spectra, suggesting possible structures.

An educational guide to exploring the world of chemistry.

Leading AI in graphene research, offering innovative insights and solutions, powered by OpenAI.

Quick quiz Q's with hints for engineers trying not to forget.

Friendly, professional interviewer for engineering roles.

Expert in engineering knowledge

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