Schrödinger provides a computational platform that aids in the research efforts of biopharmaceutical companies, academic institutions, and government laboratories around the world. Their platform offers advanced computational tools that help in drug discovery and development across various therapeutic areas. Schrödinger's products work by utilizing sophisticated algorithms and simulations to predict molecular behavior, which assists researchers in identifying potential drug candidates more efficiently. What sets Schrödinger apart from its competitors is its extensive global reach, serving clients in over 70 countries, and its dual focus on both software licensing and collaborative drug discovery programs. The company's goal is to enhance scientific research and innovation by providing powerful computational resources that accelerate the drug discovery process.

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Schrödinger

Schrödinger have been awarded $19.5m by the Bill & Melinda Gates Foundation to use their platform to accelerate drug discovery for neglected diseases.

The big global health R&D funding stories of 2024

New Methods For Drug DiscoveryDiscovering new drugs has become increasingly expensive and complex in the last few decades, with new therapies costing more than a billion dollars to be developed.This is partly because all the low-hanging fruits are already picked, like known medicinal plants and “easy” to find biochemicals.Another factor is that the diseases yet to find an efficient treatment for are the most complex ones, often caused by a body complex dysfunction (diabetes, Alzheimer's, cancer, obesity, etc.) or hard-to-reach causes (for example parasites and viruses like HIV or malaria, very good at evading the immune system).This means that for discovering new molecules now, hundreds of thousands or even millions of compounds must be considered before narrowing it down to a few.This is a daunting task, and an expensive process as well.Luckily, progress in AI and computation has made it possible to comb through astronomical volume of data, at a much lower cost in both dollars and time.And two companies are now merging to speed up the adoption of AI-driven drug discovery.Exscientia  Recursion MergerOn August 8th, 2024  the merger of Exscientia with its larger peer Recursion Pharmaceuticals was announced.Both companies had developed their own process to leverage AI and automation to speed up drug discovery, as well as reduce costs.A strong argument for the merger is that both companies' technologies are quite complementary. We will look in detail below at both companies, but the overall picture is such:Recursion is focused on “first-in-disease” opportunities, through a deeper understanding of biological mechanisms.Exscientia is focused on “best-in-class” drugs, with expertise in precision chemistry and molecular synthesis.So together, the combined company not only gets some additional scale to save money on overhead costs and regulatory compliance (usually the goal of mergers in biotech) but also the combination of excellence in chemistry synthesis AND biological insights.Exscientia The company is using AI to develop precision therapies.It runs a “full stack” AI drug discovery technology with dedicated software at every stage of the drug discovery process.So instead of looking at existing molecules, Exscientia’s Precision Design AI designs custom molecules to match the target found by its Precision Target AI.Exscientia's technology reduces 70% of the time required for going from a biological target to finding a corresponding drug and an 80% more capital-efficient process.Part of the time and cost saving comes from a highly automatized process, with “comprehensive robotic automation across the entire experimentation cycle”.This resulted in 4 compounds in early clinical stages, 30 programs in total, and $6.5B in revenues from milestones with partners. The main focus has been oncology (cancer) and inflammatory diseases.Recursion Pharma Recursion Pharmaceuticals leverages AI in drug discovery. The more AIs get involved in drug discovery and development, the more data will become precious for training the AIs.Biology is an extremely complex field, with integrated and verified data sometimes in short supply. This is a serious problem when any error will create bias, limitations, and errors in the AI, which might then need to be retrained from scratch.So creating solid datasets has been the focus of the company since its inception looking to solve several problems with biodata:Analog data, from faxes to pdf or scanned printouts.Siloed data, with little to no annotations.Hard to replicate research.To solve these problems, Recursion created one of the world's largest automated wet labs, and digitized millions of their own experiments (2.2 million experiments per week).It combines dry lab (in-silico) and wet lab (biological samples) with:A library of 1.7 million small molecules.Cell cultures, CRISPR gene editing, soluble factors, live viruses, etc.An automated laboratory robotics workflow that allows for up to 2.2 million experiments each week.High-throughput microscopes and sequencing systems.Continuous video feeds from cameras, recording holistic measurements of animal behaviors.Advanced computational resources, which have generated 21 petabytes of proprietary high-dimensional data.ADMET (absorption, distribution, metabolism, excretion, and toxicology) data.This creates unique (and massive) datasets at all levels of the “multiomics” biosciences, including proteomics (protein levels), transcriptomics (mRNA levels), phenomics (cellular morphology), ADMET and “in-vivonomics” (animal behaviors). The company is also looking to add metabolomics and genomics to its datasets in the future.(you can read more about why multiomics matters in “Multiomics Are The Next Step In Biotechnology”).So while Exscientia started by studying biological mechanisms and designing a drug for it, Recursion instead built from scratch a massive database of standardized and replicated biological research.Recursion also acquired in May 2023 the drug chemistry-focused preclinical startups, Cyclica and Valance, for a total of $87.5M.They also own one of the world's fastest supercomputers to train their LLMs and AIs for drug discovery

Recursion And Exscientia Merger Create A New Ai Drug Discovery Leader

Those discussions led to Schrödinger recently receiving a $10 million grant from the Gates Foundation toward expanding its computational platform to predict toxicity risk early in drug discovery.

With $10M from Gates Foundation, Schrödinger Takes Aim at Toxicity Risk

Schrödinger, an international science software company, has announced a $10 million grant from the Bill & Melinda Gates Foundation to launch an initiative that expands computational tools to predict toxicology risk early in drug discovery.

Gates Foundation awards $10 million for predictive toxicology tools

Schrödinger expands drug discovery platform with $10M grant, tackling toxicology challenges. New AI-powered tools aim to reduce failures, accelerate development, and improve drug safety. Learn how this impacts pharma research.

Schrödinger Launches Initiative to Significantly Expand Application of Computational Tools for Predictive Toxicology | SDGR Stock News

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The post Eonix making great progress six years after relocating to Knoxville appeared first on Teknovation.biz.

Eonix Making Great Progress Six Years After Relocating To Knoxville

DURHAM, N.C.--(BUSINESS WIRE)--Avicenna Biosciences today introduced an extension to its machine learning (ML) technology platform to enhance medicinal chemistry and expedite clinical-stage drug discovery. The company has raised $14.5 million in funding to date, with DCVC Bio leading its 2022 seed round, and this month published a paper in the peer-reviewed Journal of Chemical Information and Modeling. Co-authored with researchers from Schrödinger and Microsoft Research AI4Science, the paper outlines how combining Schrödinger’s physics-based methods with Avicenna’s novel ML methods can make the lead-to-drug optimization phase of small molecule drug discovery faster, less expensive and more successful – particularly when it comes to engineering potency and selectivity against a potential biological target.“We’re accustomed to hearing scientific success stories, but the countless failures that happen along the way often get overlooked. In medicinal chemistry especially, failure is prevalent. It can require hundreds of millions of dollars across many clinical attempts to bridge the complex gap from chemistry and biology to medicine, and successfully develop an approved drug,” said Dr. Thomas Kaiser, co-founder and Chief Scientific Officer at Avicenna

Avicenna Introduces Machine Learning-Enhanced Medicinal Chemistry Platform To Accelerate The Last Mile Of Small Molecule Drug Discovery

Summary
Schrödinger has published its second annual Corporate Sustainability report, which provides a comprehensive overview of the company's progress developing and executing on its strategy for addressing environmental, social and governance (ESG) matters.

Schrödinger Shares Corporate Sustainability Progress in 2023 Report

NEW ORLEANS--(BUSINESS WIRE)--Schrödinger, Inc. (Nasdaq: SDGR), whose physics-based computational platform is transforming the way therapeutics and materials are discovered, today presented the discovery of SGR-1505, its MALT1 inhibitor, during the First Time Disclosure Session at the American Chemical Society (ACS) Spring 2024 Meeting. SGR-1505 is being evaluated in a Phase 1 dose-escalation study in patients with relapsed/refractory B-cell malignancies. The oral presentation provided an overview of how Schrödinger leveraged its computational approaches at scale to discover SGR-1505. Schrödinger’s platform enabled vast exploration of chemical space, triaging 8.2 billion compounds, synthesizing 78 of the most promising molecules in the lead series, and identifying SGR-1505 as the company’s development candidate in approximately 10 months. Reaching a development candidate can take three to six years and typically involves synthesizing up to 5,000 molecules per program

Schrödinger Highlights Discovery Of Sgr-1505, Clinical-Stage Malt1 Inhibitor, At American Chemical Society National Meeting

Computational platform for biopharmaceutical research

Schrödinger

Computational platform for biopharmaceutical research

Schrödinger

Computational platform for biopharmaceutical research

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