Lux Capital manages over $5 billion in assets focused on founding, seed, early-stage, and growth investments at the intersection of technology and the sciences. Lux takes an active role in helping entrepreneurs build successful businesses in high-growth sectors. Its investment team has built over 20 companies from scratch. Lux's investment strategy ensures portfolio companies are better connected, have a deeper insight, and command industry leadership faster than their competitors.

Lux Capital

Amazon Web Services provides information technology infrastructure services to businesses in the form of web services. Its products and solutions include cloud computing, compute, networking, storage and content delivery, databases, analytics, application services, deployment and management, mobile services, applications, AWS marketplace software, startups, enterprises, partners, government and education, websites and web applications, big data and HPC, gaming, and digital media as well as backup, storage, and archive.   The company also provides AWS management console, documentation, SDKs and tools, downloads, platforms, Java, JavaScript, mobile, PHP, Ruby, Python, Windows, .NET, resources, forums, tutorials and articles, whitepapers, training and certification, and AWS user groups for developers. In addition, it offers compute, storage, database, analytics, application, and deployment services; and mobile services, websites, and backup and recovery services.   Amazon Web Services serves the government, education, and non-profit sectors in North America, South America, Europe, Africa, Asia Pacific, and internationally.   Amazon Web Services was founded in 2006 and is based in Seattle, Washington. It has data center locations in the United States, Europe, Brazil, Singapore, Japan, and Australia. It operates as a subsidiary of Amazon.com Inc.

Amazon Web Services

NVentures is a venture capital firm that invests in early-stage technology companies, focusing on innovations in various sectors. NVentures is the venture capital arm of NVIDIA.

NVentures

EvolutionaryScale

EvolutionaryScale is a developer of biological artificial intelligence models for therapeutic design. The business predicts protein structures by combining biological data from DNA sequences, gene expression, and epigenetic states, allowing scientists to use massive language models to create RNA-based medicinal therapies.

EvolutionaryScale develops AI models for applications in the field of biotechnology. The company's core offering, ESM3, is a GenAI model trained on a dataset of 2.78 billion proteins, which can reason over the sequence, structure, and function of proteins, enabling it to generate novel proteins for use in drug discovery, carbon capture, and materials science. The model has 98 billion parameters and is trained using 1 trillion teraflops of computing.

Foundation Models

EvolutionaryScale is an AI biotechnology company focused on developing LLMs for biology. Its flagship product, ESM3, is an AI model designed for generating novel proteins, with the ability to understand complex biological data and create new proteins that do not exist in nature. The product includes three proprietary models (98B parameter, 7B parameter, and 1.4B parameter versions) and one open-source model (1.4B parameter), which is available via Amazon SageMaker and AWS HealthOmics.&nbsp;

Artificial intelligence is identifying and testing new therapies faster than ever.

AI Drug Discovery

EvolutionaryScale Raises $142M and Unveils AI Model ESM3 to Transform Biology

To distribute EvolutionaryScale's ESM3 model to scientists and researchers, promoting advancements in drug discovery and carbon capture.

SaaS (Software-as-a-Service) companies providing their ML platform to other pharma companies for drug discovery services such as target selection, validation and lead optimization.

NVIDIA

To accelerate applications from drug discovery to synthetic biology with AI.

EvolutionaryScale develops AI models for applications in the field of biotechnology. The company's core offering, ESM3, is a GenAI model trained on a dataset of 2.78 billion proteins, which can reason over the sequence, structure, and function of proteins, enabling it to generate novel proteins for use in drug discovery, carbon capture, and materials science. The model has 98 billion parameters and is trained using 1 trillion teraflops of computing.The model has generated a new variant of the Green Fluorescent Protein (GFP), a process that is claimed to take 500 million years to occur naturally.Key customers and partnershipsIn June 2024, the company entered into partnership with AWS, to allow the ESM3 model family available on AWS's generative AI and health services, including Amazon SageMaker, Amazon Bedrock, and AWS HealthOmics.In the same month, EvolutionaryScale partnered with Nvidia to optimize ESM3 for training and inference performance. The model will be available through Nvidia's BioNeMo NIMs (Nvidia Instance Microservices) and supported by Nvidia's AI Enterprise software license.Key competitors: None comparable (No comparable companies in coverage that offers models targeting protein-generating AI models.)

<div>
 
 AI21 is an Israel-based AI research lab developing large language models (LLMs). The firm also builds GenAI applications based on its LLMs, marketing them to businesses and consumers.&nbsp;
 
 
 AI21 launched its first AI model, &ldquo;Jurassic-1,&rdquo; in August 2021, offering two versions. The larger variant, Jurassic-1 Jumbo, contained 178 billion parameters&mdash;three billion more than OpenAI&rsquo;s 175-billion parameter GPT-3 model. Similar to GPT-3, Jurassic is able to write emails and articles, summarize text, compose poetry and recipes, create website layouts, and generate code.&nbsp;
 
 
 The firm concurrently launched AI21 Studio, a platform allowing users to train custom versions of Jurassic-1 and embed them into products via an API.&nbsp;
 
 
 In May 2022, AI21 confirmed it was working on a new AI system, called the &ldquo;modular reasoning knowledge and language&rdquo; or &ldquo;MRKL&rdquo; framework (pronounced &ldquo;miracle&rdquo;). MRKL augments LLMs, connecting to external sources and symbolic reasoning models to enable the generation of accurate historical information, news on current events, and dynamic information, such as weather, exchange rates, and Wikipedia information. This allows models to accurately answer math queries by calling a calculator and navigate more complex questions by breaking down queries into parts. An initial implementation of MRKL, Jurassic-X, is available on AI21 Studio, which allows users to connect LLMs to their own databases and navigate their data using natural language.
 
 
 In March 2023, the firm launched &ldquo;Jurassic-2,&rdquo; a successor to its initial language model. The new LLM differs from Jurassic-1 and most popular LLMs, in that it incorporates semantic reasoning into its text generation processes alongside statistical inference, enabling the model to more reliably and accurately respond to queries.
 
 
 AI21 says this was achieved by training Jurassic-2 in two phases: A traditional self-supervised phase using an extensive corpus of unstructured and unlabeled data and a second phase, involving a large volume of labeled data to teach the model to follow instructions. Jurassic-2 did not initially implement the MRKL framework, although AI21 confirmed it as an upcoming feature.
 
 
 The company claims that its foundation models are 30% more efficient than those of other providers and it also does not charge additional fees for the use of fine-tuned models.
 
 
 
 
 In March 2024,
 <a href="https://sp-edge.com/updates/27995">
 AI21 launched Jamba,
 </a>
 a model that integrates the Mamba Structured State Space model (SSM) technology and transformer components. The company further introduced 
 <a href="https://sp-edge.com/updates/32879">
 Jamba 1.5 Mini and Jamba 1.5 Large
 </a>
 to offer high performance and efficiency for long-context language processing in August 2024. The models have a hybrid architecture combining Transformer and Mamba approaches, allowing for quality responses with large context windows.
 
 
 Key customers and partnerships
 
 
 As of June 2023, AI21&rsquo;s customers include Latitude, Easyway, Verb, Tweet Hunter, and Harambee.
 
</div>

AI21 Labs

UK-based Stability AI is actively involved in building AI applications and offers a range of foundation models, including:Text-to-image modelStable Diffusion (SDXL 1.0): The open-source text-to-image model incorporates a dual network architecture with a 3.5-billion parameter base model and a 6.6-billion parameter refiner. The model was trained on image-caption pairs from the LAION-5B dataset derived from Common Crawl data. It excels in high-quality image generation, spatial object positioning, and complex concept recognition. The model can be fine-tuned for specific tasks using techniques such as embeddings, hypernetworks, and tools like DreamBooth, a deep-learning generation model.<a href="https://sp-edge.com/updates/30420">Stable Diffusion 3 Medium:</a> a text-to-image open-source AI models with 2 billion parameters utilizing a Diffusion Transformer Architecture and can be fine-tuned using small datasets.<a href="https://sp-edge.com/updates/32260">Stable Fast 3D:</a> Text-to-image model generates 3D images from a single 2D image.In November 2023, the company launched <a href="https://sp-edge.com/updates/23352">two new products</a> Sky Replacer and Stable 3D. Sky Replacer lets users manipulate the aesthetic of the sky in their photos from a choice of nine alternatives, enhancing the overall image appeal and Stable 3D aims to simplify the process of creating textured 3D objects, from images or text prompts, into a quick, automatic activity.Text-to-text modelsStable Beluga 1: The instruction-following language model is based on LLaMA 65 billion and has reasoning capabilities.Stable Beluga 2 (FreeWilly): An expansion to Stable Beluga 1, this model is based on LLaMA 70 billion and reportedly ranks near the top of the Open LLM Leaderboard.StableLM is an open-source language model suite; the company has released alpha versions of three billion- and seven-billion parameter models, with 15 to 65 billion-parameter models to follow. It's designed to generate text and code, and can power various downstream applications. <a href="https://sp-edge.com/updates/25750">Stable LM 2 1.6B, </a>an AI language model designed for multiple languages, was released in January 2024.The Japanese StableLM Alpha, is a seven-billion parameter Japanese language model, which claims to have superior performance compared to other publicly available Japanese language models. It offers text generation capabilities and is released under the Apache License 2.0 for commercial use, with an additional research version available for research purposes.StableCode has three billion parameters and is specifically designed to help programmers with coding tasks by providing autocomplete suggestions and responses to programming instructions.Text-to-audio modelStable Audio, which has 907 million parameters, uses a latent diffusion model architecture for audio and is conditioned on various factors including text metadata, audio file duration, and start time. This conditioning allows control over the content and length of the generated audio.Text-to-image model<a href="https://sp-edge.com/updates/26910">Stable Diffusion 3,</a> which has 800 million range and 8 billion parameters and combines a diffusion transformer architecture and flow matching. Additionally, the model can depict multi-subject prompts, create images, and correct spelling. <a href="https://sp-edge.com/updates/30068">Public preview</a> of this model was announced in June 2024.<a href="https://sp-edge.com/updates/26645">Stable Cascade,</a> a text-to-image model developed with a three-phase approach for image creation and modification.Text-to-video model<a href="https://sp-edge.com/updates/24289">Stable Video Diffusion</a>, an AI model designed to produce videos from images, offered as a research preview. The models, SVD and SVD-XT, can generate short, high-quality clips but come with limitations.<a href="https://sp-edge.com/updates/27695">Stable Video 3D (SV3D), </a>a GenAI tool to create 3D videos to create multi-view 3D models from a single input image and can produce short video content from image or text prompts.<a href="https://sp-edge.com/updates/31945">Stable Video 4D,</a> a model that enables users to upload video and obtain dynamic videos from different views.There has been considerable debate about the ethical and copyright implications of using stable diffusion to create images. The model is particularly permissive when it comes to creating different types of content, which has raised concerns about its potential for abuse. In response to its permissiveness, Stability AI has become involved in copyright infringement lawsuits, with artists and Getty Images raising legal objections to the use of their content in the model's training data.In March 2024, <a href="https://sp-edge.com/updates/27872">Stability AI's founder and CEO, Emad Mostaque, resigned from his role </a>and the company's board to focus on the development of decentralized AI to bring transparency and competitiveness to AI.In April 2024, the company made Stable Diffusion 3 and Stable Diffusion 3 Turbo available on the <a href="https://sp-edge.com/updates/28612">Stability AI developer platform API.</a>In May 2024, Stability AI <a href="https://sp-edge.com/updates/29321">introduced Stable Artisan,</a> a tool to enable media generation for Discord users.Key customers and partnershipsIn December 2022, Stability AI entered a partnership with Amazon, with AWS designated as the preferred cloud provider to facilitate the development and expansion of Stability AI's AI models.In March 2024,<a href="https://sp-edge.com/updates/27403"> Stability AI partnered with Tripo AI,</a> a provider of AI 3D models, to release TripoSR for 3D object reconstruction to cater to the demands of entertainment, gaming, industrial design, and architecture professionals, with responsive outputs for visualizing detailed 3D objects.In April 2024, the company<a href="https://sp-edge.com/updates/28612"> partnered with Fireworks AI,</a> a provider of API platforms, to deliver Stable Diffusion 3 and Stable Diffusion 3 Turbo for enterprises.In September 2024, the company <a href="https://sp-edge.com/updates/33262">launched Stable Image Ultra, Stable Diffusion 3 Large, and Stable Image Core</a>, its text-to-image models on Amazon Bedrock.

Stability AI

Cohere is a Canadian startup founded in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst, with backgrounds in natural language processing and prior experience at Google Brain. The firm specializes in the development and deployment of large language models (LLMs) for various applications across enterprise and business use cases. Its foundation model, Cohere Command, is a large natural language text generation model with 52.4 billion parameters, focused on task-oriented dialogue, creating summarization, copywriting, dialogue extraction, and question answering.Cohere also provides Cohere Embed, a model designed to generate vector embeddings that capture the essence of text, improving capabilities in search, content moderation, and intent recognition. These embeddings help empower Cohere's multilingual customizable models, which can be fine-tuned to cater to specific use cases and industries. Additionally, Cohere ensures secure deployment options, including private cloud solutions, for added data security and privacy. In November 2023, Cohere unveiled <a href="https://sp-edge.com/updates/23446">Embed V3</a> with advanced capabilities in matching documents to queries, increasing the efficiency of retrieval augmented generation, and reducing the operational costs of LLM applications. In February 2024, the company introduced <a href="https://sp-edge.com/updates/26583">Aya,</a> an open-source LLM that supports 101 languages.According to Stanford University's HELM benchmarks, Cohere's Command model ranks among the highest-performing models available.In March 2024,<a href="https://sp-edge.com/updates/27502"> Cohere released an advanced language model, “Command-R,</a>” for enterprises. The model offers improved performance on retrieval augmented generation (RAG) and tool usage, with longer context windows of up to 128,000 tokens.In April 2024, <a href="https://sp-edge.com/updates/28198">the company released an advanced language model, Command R+</a>, for business applications. The model offers enhanced retrieval augmented generation (RAG) with citation, supports 10 business languages, and automates workflows. Additionally, <a href="https://sp-edge.com/updates/28463">Cohere also released Rerank 3,</a> a model designed to enhance enterprise search and RAG systems. In the same month, <a href="https://sp-edge.com/updates/28509">the company also introduced a private beta for Cohere Compass,</a> a foundation embedding model designed to allow multi-aspect data indexing and searching, and also <a href="https://sp-edge.com/updates/28789">launched a toolkit available for developers to build AI applications</a> faster, offering production-ready applications that can be deployed across cloud providers, with enhanced security and customization options.In May 2024, Cohere introduced <a href="https://sp-edge.com/updates/29350">Command R Fine-Tuning</a> for enhanced performance for business application workload and<a href="https://sp-edge.com/updates/29761"> released Aya 23, </a>a multilingual language model capable of handling 23 languages,available in 8 billion and 35 billion parameters.<a href="https://sp-edge.com/updates/34141">In September 2024</a>, Cohere released updated versions of its APIs for its AI models Chat, Embed, Rerank, and Classify. These updates, collectively called “API V2,” introduced several upgrades to its existing models, including mandatory model version specification in API calls, consolidation of input parameters for Chat API, and improved tool integration capabilities.&nbsp;Key customers and partnershipsIn November 2021, Cohere partnered with Google Cloud to leverage its infrastructure support and TPUs for product development and deployment. Cohere also partnered with other major cloud providers like AWS and Oracle.In March 2024,<a href="https://sp-edge.com/updates/27307"> Cohere partnered with consultancy firm Accenture</a> to integrate its Command model and retrieval augmented generation (RAG) capabilities to offer GenAI solutions for businesses.In April 2024, <a href="https://sp-edge.com/updates/28932">Cohere partnered with Amazon </a>to incorporate Command R+ and Command R to Amazon's cloud platform, Bedrock.In June 2024, the company<a href="https://sp-edge.com/updates/30492"> partnered with Borderless AI,</a> a provider of AI-powered HR solutions, to create tailored AI agents for HR tasks.The company entered into a <a href="https://sp-edge.com/updates/31052">strategic partnership with i-5O,</a> a provider of AI solutions for manufacturing, as part of Cohere's Startup Program and also partnered with <a href="https://sp-edge.com/updates/31590">Fujitsu</a> in July 2024 to develop a Japanese LLM.In September 2024, Cohere <a href="https://sp-edge.com/updates/33458">partnered with Nomura Research Institute (NRI)</a>, a Japanese consulting firm, to develop the NRI Financial AI Platform.

Cohere

Founded in 2022, Hippocratic AI is a healthcare-focused AI model developed by Health GPT. At its core, Hippocratic AI uses a state-of-the-art (SOTA) large language model (LLM) designed to improve healthcare processes, particularly in non-diagnostic tasks. Hippocratic AI aims to differentiate itself from traditional LLMs by prioritizing pre-training with trusted, evidence-based healthcare content rather than the broader Internet. Claimed to be the first safety-focused LLM for healthcare, the model undergoes a unique Reinforcement Learning with Human Feedback process, known as RLHF-HP, which involves working with healthcare professionals to validate its readiness for use. In addition, the model aims to incorporate a compassionate “bedside manner” in its interactions with patients, recognizing the impact of empathy on emotional wellbeing and health outcomes.According to the company, Hippocratic AI outperformed GPT-4 in majority of tests and certifications. Out of 114 evaluations, it outperformed GPT-4 in 105 cases, with a notable margin of +5% in 74 evaluations and +10% in 43 certifications.Key customers and partnerships&nbsp;In August 2023, the company launched the Hippocratic AI Founding Partner Program, an initiative through which the company collaborates with leading healthcare organizations to become founding partners and actively participate in the development and validation of Hippocratic AI's LLM and technology. The program includes establishing use cases, ensuring clinical and patient safety, and contributing to the advancement of healthcare-specific AI applications. In February 2024, these companies began <a href="https://sp-edge.com/updates/27145">beta</a> testing their LLM for non-diagnostic applications in healthcare.In March 2024, <a href="https://sp-edge.com/updates/27696">Hippocratic AI partnered with NVIDIA</a> to develop AI healthcare agents that have low-latency conversational interactions with patients.

Hippocratic AI

<div>
 
 Founded in 2023, GenHealth.ai is a startup focused on advancing healthcare solutions through the use of GenAI. Its proprietary approach involves training a foundation transformer model, known as DOOG-E, on extensive healthcare data, including 50+ million of coded patient claims and clinical information. DOOG-E is a specialized Generative Pre-Trained Transformer (GPT) AI designed for healthcare applications, with 70 million parameters and one billion tokens. Unlike traditional large language models, DOOG-E is a large medical model (LMM) that uses medical events such as ICD, CPT, RxNorm, and LOINC codes as its &quot;language&quot; to predict future sequences of health events based on historical data.
 
 
 The company claims that its LMM, trained on medical data, is less prone to bias and hallucination compared to its GenAI, large language models (LLM), rendering it more dependable for healthcare applications.
 
 
 Funding and financials&nbsp;
 
 
 In July 2023, the company raised USD 13 million in early funding co-led by Craft Venture and Obvious Ventures. It planned to use the funds to support the development and expansion of its LMM for various healthcare applications, including risk adjustment, care management, and financial benchmarking.
 
 
 
</div>

GenHealth

Foundation models that can handle a range of natural language processing (NLP) use cases. Typical tasks include generating summaries, translating, answering questions, and classifying text. These models typically use text inputs and generate text outputs.

Fine-tuned versions of large language models, which are focused on specific tasks and applications (e.g. code generation, medical event prediction, financial NLP tasks, etc). These are usually developed by training LLMs using custom data and also serve as foundation models that can be developed even further. These models typically use text inputs and can generate text and/or code outputs.

Foundation models that translate visual data based on features and contextual information identified during training. This enables models to interpret images and video, applying those interpretations to predictive or decision-making tasks. Typical tasks include image recognition and classification into categories. These models typically use image and/or video inputs and generate text outputs.

Foundation models that are trained to understand and work with two or more forms of input data (e.g., text and images) to solve problems and complete tasks using both natural language processing and computer vision. These models can also generate outputs across textual and visual modalities. Typical tasks include visual question answering, image captioning, and generating images based on text descriptions. These models typically use text and/or visual inputs and generate text and/or visual outputs.

Foundation models that use big data to suggest or recommend additional products to consumers. These models are designed to predict user-item interaction probabilities and provide recommendations to users. The models are trained to make these inferences based on historical data on customer interactions and non-interactions. These models typically use user features, item features and user interactions as inputs, and generate numeric values like ratings predictions and matching probabilities as outputs.

Foundation models that are capable of automatic speech recognition, transcription in multiple languages, and translation from these languages to English. These are sometimes referred to as audio models. These models typically use speech/audio as inputs and generate text outputs.

Foundation models that transform user text prompts into video clips.

Foundation models that generate music from text descriptions.

All companies within the space are building large AI models

Companies in this space leverage NLP to train LLMs

The hub exclusively covers companies that have built foundation models

All companies within the space can leverage their foundation models for generative AI applications

The technology enables the creation of personalized content

EvolutionaryScale is an AI biotechnology company focused on developing LLMs for biology. Its flagship product, ESM3, is an AI model designed for generating novel proteins, with the ability to understand complex biological data and create new proteins that do not exist in nature. The product includes three proprietary models (98B parameter, 7B parameter, and 1.4B parameter versions) and one open-source model (1.4B parameter), which is available via Amazon SageMaker and AWS HealthOmics.&nbsp;ESM3’s capabilities include generating complex, multi-domain proteins from scratch, creating protein design workflows, incorporating antibody understanding, and enabling the engineering of proteins based on desired functionalities. The model has already been used to generate a new variant of the green fluorescent protein (GFP), a marker protein that can indicate another protein with fluorescence, enabling scientists to see its presence in an organic structure.&nbsp;Key customers and partnershipsEvolutionaryScale has formed partnerships with industry leaders such as <a href="https://sp-edge.com/updates/30848">Amazon Web Services (AWS)</a> and NVIDIA to accelerate the adoption and deployment of ESM3. Through collaboration with AWS, EvolutionaryScale aims to make ESM3 accessible to researchers worldwide.

<div>
 
 HiberCell is a biotechnology company that develops novel therapies focused on oncology. It entered the AI drug discovery space when it acquired AI genomics analysis company Genuity Science (and its subsidiaries in Iceland and Ireland) in August 2021.&nbsp;
 
 
 Genuity Science&rsquo;s solutions include end-to-end therapeutic discovery and development services such as building custom disease datasets, discovering and prioritizing targets, identifying biomarkers, and optimizing clinical trials. Genuity Science leverages its AI-powered platform to manage, process, and analyze genomic data. HiberCell intends to use Genuity Science&rsquo;s AI/ML capabilities to advance its oncology pipeline of five programs, of which one has reached the Phase 2 clinical trial stage.
 
 
 As of March 2024, the company&rsquo;s pipeline included three drug candidates, namely: 1) HC-7366, targeting the general control nonderepressible 2 (GCN2) kinase as part of the integrated stress response (ISR) signaling pathway; 2) HC-5404, aimed at inhibiting the PKR-like endoplasmic reticulum kinase (PERK) involved in the unfolded protein response (UPR) signaling pathway; and 3) odetiglucan, a therapy targeting tumor-immune interactions within the tumor microenvironment​.&nbsp;
 
 
 Key customers and partnerships
 
 
 In December 2023, HiberCell and Merck &amp; Co partnered to conduct a Phase Ib trial to evaluate the combination therapy of belzutifan and HC-7366 for treating clear cell renal cell carcinoma. Further, in January 2021, the company partnered with Biodesix to develop an enzyme-linked immunosorbent assay as a companion diagnostic test.
 
 
 Funding and financials
 
 
 In 
 <a href="https://hibercell.com/news/hibercell-closes-67-4-million-series-b-financing-and-secures-30-million-debt-facility-from-hercules-capital/">
 May 2021
 </a>
 , HiberCell completed a Series B financing round, raising USD 67.4 million, and secured an additional USD 30 million in debt financing from Hercules Capital. The funds were expected to be used for clinical advancement of its novel therapeutic candidates. 
 
</div>

HiberCell

<div>
 
 Arbor Biotechnologies specializes in gene editing to develop novel therapeutics for a variety of indications, starting with liver-related diseases. It operates an AI-powered biodiscovery platform to discover novel CRISPR enzymes, which are sequences of repeating genetic code. To do this, its platform deploys computational search techniques, genome sequencing, gene synthesis, and high-throughput screening. This can eventually be turned into drugs or even applied to agriculture and other industrial uses. In 2018, the company announced the discovery of Cas13d, a new type of gene editing enzyme that helps to alter RNA to treat genetic diseases.&nbsp;
 
 
 As of February 2024, the company had five internal drug discovery programs, of which one was in the IND-enabling stage. It also has multiple collaborative programs under development focused on oncology and various other therapeutic areas.
 
 
 Key customers and partnerships
 
 
 The company has a long-standing partnership with Vertex Pharmaceuticals since
 <a href="https://www.businesswire.com/news/home/20190103005194/en/Vertex-and-Arbor-Biotechnologies-Establish-Collaboration-to-Discover-Novel-Proteins-to-Advance-Discovery-of-Gene-Editing-Therapies">
 January 2019
 </a>
 , which focused on developing gene-editing treatments for cystic fibrosis and four other diseases. The alliance was further extended in 
 <a href="https://www.biospace.com/article/vertex-and-arbor-strike-a-1-2-billion-deal-for-gene-editing-programs/">
 August 2021
 </a>
 and 
 <a href="https://sp-edge.com/updates/15741">
 January 2023
 </a>
 .&nbsp;
 
 
 Other partnerships included a collaboration with 
 <a href="https://www.prnewswire.com/news-releases/arbor-biotechnologies-announces-collaboration-with-ginkgo-bioworks-to-advance-the-discovery-and-development-of-precision-gene-editors-302011991.html">
 Ginkgo Bioworks
 </a>
 (December 2023) to leverage Ginkgo's R&amp;D services to further optimize Arbor's novel precision editors, a non-exclusive licensing agreement with 
 <a href="https://www.globenewswire.com/news-release/2022/02/08/2380680/0/en/EdiGene-and-Arbor-Biotechnologies-Announce-Worldwide-Non-exclusive-License-Agreement-to-Advance-Ex-Vivo-Engineered-Cell-Therapy-Programs.html">
 EdiGene
 </a>
 (February 2022) to leverage Arbor&rsquo;s platform for ex vivo oncology therapies, a strategic research and non-exclusive licensing agreement with 
 <a href="https://investors.tcr2.com/news-releases/news-release-details/tcr2-therapeutics-and-arbor-biotechnologies-establish">
 TCR2 Therapeutics
 </a>
 (January 2022) to develop allogeneic TRuC T-cell therapies using Arbor&rsquo;s CRISPR technology, and a license agreement with 
 <a href="https://www.lonza.com/news/2020-12-23-14-00">
 Lonza
 </a>
 (December 2020) to use Arbor&rsquo;s gene-editing technology for Lonza&rsquo;s bioproduction products and services.
 
 
 Funding and financials
 
 
 In its latest Series B funding round in November 2021, which was led by Ally Bridge Group, TCG Crossover, and Temasek, Arbor Biotechnologies raised USD 215 million. The proceeds of the round were earmarked to fuel the company&rsquo;s clinical development pipeline.
 
</div>

Arbor Biotechnologies

Based in the UK, Arctoris provides an automated drug discovery platform, offering fully validated research and development (R&amp;D) processes under an Experiments-as-a-Service model. The company works with biotechnology companies, AI drug discovery companies, pharma and academic institutions enabling these institutions to run experiments in Arctoris’s platform and provides on-demand access to a broad scale of datasets for AI drug discovery companies to train their algorithms.

Arctoris

<div>
 
 Frontier Medicines leverages AI/ML technologies to develop treatments for cancer-causing proteins that are dismissed by traditional pharma companies as undruggable.&nbsp;
 
 
 The company has a proprietary chemoproteomic platform which entails an approach based on the interaction of small-molecule compounds and proteins to screen a large number of targets and combine them with a protein database that contains more than 150,000 novel binding pockets (hotspots). This enables it to access 90% of the human proteome that were not previously targeted due to lack of hotspots. The platform&rsquo;s AI/ML engine incorporates a library of diverse compounds, a hotspot map that serves as the starting point for understanding the druggability of a potential therapeutic target, and lead design and generation.&nbsp;
 
 
 The company focuses on oncology and immunology target and, as of February 2024, it had five wholly-owned therapeutic programs and three AbbVie-partnered programs in its pipeline. All programs were in their discovery stage, except the company&rsquo;s lead candidate, FMC-376 (activated and inactivated forms), focused on treating different cancer types, including non-small cell lung cancer (NSCLC), colorectal carcinoma (CRC), and pancreatic ductal adenocarcinoma (PDAC), which is in the IND-enabling stage.
 
 
 Key customers and partnerships
 
 
 The company entered a USD 100 million partnership with 
 <a href="https://news.abbvie.com/2020-12-02-AbbVie-and-Frontier-Medicines-Establish-Global-Partnership-to-Discover-and-Develop-Novel-Therapies-and-E3-Degraders-Against-Difficult-to-Drug-Targets">
 AbbVie
 </a>
 In December 2020 to develop small-molecule therapeutics against hard-to-drug protein targets including around 600 E3 ligases (the enzymes that drive the ubiquitination and degradation of proteins.&nbsp;
 
 
 Funding and financials
 
 
 In July 2021, the company raised USD 88.5 million in a 
 <a href="https://sp-edge.com/updates/4347">
 Series B
 </a>
 funding round co-led by Woodline Partners LP and RA Capital Management, with equal participation from Deerfield Management Company. The proceeds were earmarked for advancing the company&rsquo;s drug pipeline and expanding operations in Boston, where its new facility was to complement the company&rsquo;s headquarters in California. 
 
</div>

Frontier Medicines

<div>
 
 Verge Genomics is a biotechnology company engaged in developing therapeutics for neurological diseases using human genomics and AI to accelerate drug discovery. The main target areas are Amyotrophic Lateral Sclerosis (ALS), Parkinson&rsquo;s Disease (PD), and Frontotemporal Dementia (FTD).&nbsp;
 
 
 It has built a proprietary all-human platform (unlike standard clinical trials that rely on animal data), called CONVERGE, that uses data sets from brain tissue samples of deceased patients with ALS and PD. While most drugs target only one gene at a time, the company claims its technology can identify hundreds of genes that cause disease and develop drugs that target all the genes at the same time. The company uses AI technology to optimize the drug discovery process as a whole.
 
 
 As of March 2024, Verge had one drug candidate in the clinical stage (Phase I) and 10 drug candidates in the discovery stage.&nbsp;
 
 
 Key customers and partnerships
 
 
 The company has partnerships with various tissue banks (such as Harvard University and University of Miami Miller School of Medicine) and several educational institutes and foundations (such as Target ALS, Answer ALS) that enabled its drug discovery platform.&nbsp;
 
 
 In addition, the company&rsquo;s customers include several pharmaceutical companies, such as 1) 
 <a href="https://www.vergegenomics.com/news-blog/verge-genomics-announces-research-collaboration-with-the-sheffield-institute-for-translational-neuroscience">
 Sheffield Institute for Translational Neuroscience (SITraN)
 </a>
 to accelerate the validation of novel drug targets and screening of drug compounds for Parkinson's disease (February 2021), 2) 
 <a href="https://sp-edge.com/updates/4146">
 Eli Lilly
 </a>
 to research and develop novel therapies to treat ALS (July 2021), 3) 
 <a href="https://sp-edge.com/updates/21707">
 Alexion
 </a>
 to identify novel drug targets for rare neurodegenerative and neuromuscular diseases (September 2023), and 4) 
 <a href="https://www.vergegenomics.com/news-blog/verge-genomics-and-the-pritzker-neuropsychiatric-disorders-research-consortium-form-cutting-edge-schizophrenia-research-collaboration">
 Pritzker Neuropsychiatric Disorders Research Consortium
 </a>
 to analyze data from schizophrenic patient&rsquo;s brain tissue with Verge&rsquo;s technology (October 2023).
 
 
 Funding and financials
 
 
 In December 2021, Verge raised USD 98 million in an oversubscribed 
 <a href="https://sp-edge.com/updates/7426">
 Series A
 </a>
 funding round led by BlackRock. The funds were used to scale up its pre-clinical and clinical candidate programs in the pipeline and enhance its lead therapeutics through clinical testing. 
 
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Verge Genomics

SaaS (Software-as-a-Service) companies providing their ML platform to other pharma companies to design and run preclinical experiments.

SaaS (Software-as-a-Service) companies providing their ML platform to other pharma companies to identify biomarkers (measurable biological signs used to determine disease progression) and predict drug response.

SaaS (Software-as-a-Service) companies aggregating and analyzing biomedical data with the help of AI to generate insights across the drug development pipeline.

Biotechnology companies that have integrated AI as a core component of their in-house drug development process. They typically have a few in-house drug development programs, and also collaborate with pharma companies to develop certain drugs.

Includes companies that use GenAI as a core part of their product offering for drug discovery & development

Companies leverage GenAI to support drug discovery through accelerated drug designing

Companies offer AI/ML-based SaaS products to accelerate the discovery and development of novel therapeutics

Offers AI/ML-based solutions to accelerate drug discovery and development

PROMINENT_CATEGORY

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 AI research lab EvolutionaryScale launches ESM Cambrian, a cutting-edge language model for protein sequence analysis. ESM Cambrian comes in three versions with 300 million, 600 million, and 6 billion parameters respectively.
 This model utilizes masked language modeling to forecast amino acids in protein sequences. The training process is two-fold: initially with a context length of 512 for one million steps, then with a lengthened context of 2048 for 500,000 steps. Training data is sourced from UniRef, MGnify, and the Joint Genome Institute, clustered at 70% sequence identity level.
 In comparison to its predecessor ESM2, ESM Cambrian presents notable performance enhancements. The 300-million parameter variant meets ESM 2 650 million's performance while reducing memory usage, and the 600-million parameter model competes with the 3 billion ESM2. The 6-billion parameter model surpasses all previous ESM2 models, establishing a new standard in efficiency.
 
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