Best AI tools for< Identify New Drug Candidates >

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.

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.

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.

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.

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.

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.

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.

The website is an AI tool that offers a wide range of AI, software, and tools for enterprise growth and automation. It provides solutions in areas such as AI hardware, automation, application security, CRM, cloud services, data management, generative AI, network monitoring, process intelligence, proxies, remote monitoring, surveys, sustainability, workload automation, and more. The platform aims to help businesses leverage AI technologies to enhance efficiency, security, and productivity across various industries.

Owkin is a full-stack AI biotech company that integrates the best of human and artificial intelligence to deliver better drugs and diagnostics at scale. By understanding complex biology through AI, Owkin identifies new treatments, de-risks and accelerates clinical trials, and builds diagnostic tools to reduce time to impact for patients.

Trade Foresight is a data and AI-driven platform that helps businesses expand and grow their operations globally. It provides users with access to a global trade database, market intelligence, and AI-powered tools to identify opportunities, connect with partners, and facilitate trade transactions. With Trade Foresight, businesses can gain insights into global trade trends, regulations, and opportunities, and make informed decisions to optimize their international expansion strategies.

Tremello is a market research platform that uses AI to deliver off-market data. It combines a leading AI engine with human experts to provide bespoke intelligence delivered directly to the user's inbox. Tremello's AI analyzes relationships, identifies patterns, and considers the broader context, delivering meaningful and actionable insights on top of a base human layer. It leverages a diverse range of data sources, including public and private databases, industry reports, social media archives, company websites, and government filings, ensuring a complete and comprehensive picture of the research subject.

VenturusAI is a tool that provides instant feedback on your business ideas. It uses GPT-3.5 and GPT-4 to generate an analysis of your idea and give you feedback on how to make it successful. The tool offers a comprehensive business analysis, including SWOT, PESTEL, and Porter's Five Forces assessments. It also provides valuable insights into your target audience, complete with user stories and demographic data. Additionally, VenturusAI offers business strategy recommendations, framework suggestions, and requirements analysis. It also explores marketing strategy and branding advice, including slogan ideas and social media post examples. The tool is user-friendly and easy to navigate, making it the perfect tool for any business owner or entrepreneur looking to take their ideas to the next level.

Beyond Limits is an industrial-grade, hybrid artificial intelligence company built for the most demanding sectors. Beyond traditional AI, Beyond Limits’ unique Hybrid AI technology combines numeric techniques like machine learning with knowledge-based reasoning to produce actionable intelligence.

The Value Chain Generator® is an AI & Big Data platform for circular bioeconomy that helps companies, waste processors, and regions maximize the value and minimize the carbon footprint of by-products and waste. It uses global techno-economic and climate intelligence to identify circular opportunities, match with suitable partners and technologies, and create profitable and impactful solutions. The platform accelerates the circular transition by integrating local industries through technology, reducing waste, and increasing profits.

ProductFlint is an all-in-one platform designed to help Etsy sellers grow their businesses. It offers a range of tools to help with keyword research, listing optimization, competitor analysis, and more. ProductFlint's AI-powered tools can help sellers create more effective listings, identify new opportunities, and stay ahead of the competition.

Google Lens is an AI-powered visual search tool developed by Google that allows users to search, shop, translate, and identify objects using their camera or images. With Google Lens, users can find similar clothes, furniture, and home decor, translate text in real-time from over 100 languages, get step-by-step homework help for various subjects, and identify plants and animals. The application is available on all devices and in various Google apps, making it convenient for users to access its features anytime, anywhere.

Notably is a research synthesis platform that uses AI to help researchers analyze and interpret data faster. It offers a variety of features, including a research repository, AI research, digital sticky notes, video transcription, and cluster analysis. Notably is used by companies and organizations of all sizes to conduct product research, market research, academic research, and more.

HostAI is an AI-powered tool designed to help vacation rental and hotel businesses automate their operations and improve guest experiences. It offers a range of features, including automated guest messaging, handling phone calls with AI, automating calendar messages, identifying gap nights, and filtering for urgent matters. HostAI integrates with various property management systems and vacation rental platforms, making it easy for businesses to connect their existing tools and streamline their operations.

Retorio is a cutting-edge Behavioral Intelligence (BI) Platform that fuses machine learning with scientific findings from psychology and organizational research to ultimately take learning and development to a new level within organizations. At the core of Retorio’s capabilities are its AI-powered immersive video simulations. Through these engaging role-plays, learners using Retorio get to train and develop the necessary skills through realistic scenarios. Furthermore, the personalized, on-demand feedback learners receive allows for immediate behavior change and performance improvement. Retorio’s training platform transcends the limitation of scalability and redefines how individuals and teams train and develop, bringing talent development to a new dimension.

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 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**

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.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

Auto_Jobs_Applier_AIHawk is an AI-powered job search assistant that revolutionizes the job search and application process. It automates application submissions, provides personalized recommendations, and enhances the chances of landing a dream job. The tool offers features like intelligent job search automation, rapid application submission, AI-powered personalization, volume management with quality, intelligent filtering, dynamic resume generation, and secure data handling. It aims to address the challenges of modern job hunting by saving time, increasing efficiency, and improving application quality.

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.

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.

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.

Inbox Zero is an open-source email app that helps you reach inbox zero fast with AI assistance. It offers various features such as a newsletter cleaner, AI assistant for auto-responding, archiving, labeling, and forwarding emails, a cold email blocker, email analytics, tracking of new senders and unreplied emails, and a large email finder to free up space. Inbox Zero is built with Next.js, Tailwind CSS, Prisma, Tinybird, Upstash, and Turbo.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

Boxcars is a Ruby gem that enables users to create new systems with AI composability, incorporating concepts such as LLMs, Search, SQL, Rails Active Record, Vector Search, and more. It allows users to work with Boxcars, Trains, Prompts, Engines, and VectorStores to solve problems and generate text results. The gem is designed to be user-friendly for beginners and can be extended with custom concepts. Boxcars is actively seeking ways to enhance security measures to prevent malicious actions. Users can use Boxcars for tasks like running calculations, performing searches, generating Ruby code for math operations, and interacting with APIs like OpenAI, Anthropic, and Google SERP.

The Synthetic-Voice-Detection-Vocoder-Artifacts repository provides the LibriSeVoc dataset containing self-vocoding samples created with six state-of-the-art vocoders to expose and exploit vocoder artifacts. It also introduces a new approach for detecting synthetic human voices by identifying signal artifacts left by neural vocoders and enhancing the RawNet2 baseline. The repository includes a paper and dataset for further reference and offers instructions for training the model and testing it in the wild.

This tool is a bot that monitors the performance of MEV transactions on the Ethereum blockchain. It provides real-time data on MEV profitability, transaction volume, and network congestion. The bot can be used to identify profitable MEV opportunities and to track the performance of MEV strategies.

Album AI is an experimental project that uses GPT-4o-mini to automatically identify metadata from image files in the album. It leverages RAG technology to enable conversations with the album, serving as a photo album or image knowledge base to assist in content generation. The tool provides APIs for search and chat functionalities, supports one-click deployment to platforms like Render, and allows for integration and modification under a permissive open-source license.

LLMLingua is a tool that utilizes a compact, well-trained language model to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models, achieving up to 20x compression with minimal performance loss. The tool includes LLMLingua, LongLLMLingua, and LLMLingua-2, each offering different levels of prompt compression and performance improvements for tasks involving large language models.

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Let's get started by telling me about your business: What do you offer? Who do you serve? ------------------------------------------------------- Need help Prompt Engineering? Reach out on LinkedIn: StephenHnilica

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