Hey Everyone,
I’m a huge fan of how A.I. is already transforming drug discovery and drug development. While it’s early days, from now on A.I. and Generative A.I. are essential ingredients to the future of A.I. discovery, development and screening.
We are going to take an amazing deep dive into this today with our guest post by our 1st time contributor.
I’m personally obsessed with startups and in the AI in drug discovery, development and screening space there are a lot of new names to follow.
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A 🧬 Golden Age of AI in Drug Discovery, Development and Screening is Nearly Here
Drug screening refers to the process by which potential drugs are identified and optimized before selection of a candidate drug to progress to clinical trials. It can involve screening large libraries of chemicals for a particular biological activity in high-throughput screening assays.
I asked to guide us through a look at some of the relevant startups in the space. She is the writer and scientist behind MetaphysicalCells, a Newsletter I refer to often since I need to know about the startups and Venture Capital funding related to the ecosystem.
While A.I. hype has focused on Generative A.I. in 2023, a lot has been happening in the biotechnology and drug development space. Both as an investor and curious A.I. enthusiast, you might find the information salient to your understanding of the future of biotech and pharma. Genomics and synthetic biology eventually will have a bright future.
Marina (based in Greece) is a cellular and molecular biologist with over 20 years of experience in academia, startups and multinationals. Her professional experience spans a wide range of areas: Cancer research, Preclinical drug development (small molecules, peptides), Cancer biomarker discovery research, Molecular diagnostics industry experience and Clinical project management. Currently, she is a Life Science Consultant offering solutions throughout product development.
MetaphysicalCells is a newsletter about Science, Technology and AI Drug Discovery by Marina T Alamanou. I hope you enjoy the topic and it leads into a rabbit hole of untold innovation that’s occurring that will likely change the future of pharma, biotechnology, genomics, computational biology and synthetic biology to unusual degrees in just a few decades in the 21st century.
Marina is also among the most generous of peers I’ve ever met on Substack. Her subject knowledge and coverage of startups in the drug discovery space is in my mind unparalleled.
By October, 2023. Kérkira, Ionian Islands, Greece
Using AI to improve efficiency and effectiveness during drug screening
An overview of AI/ML tools, startups and companies transforming drug screening
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🧬 Why This Matters – AI/ML tools, startups and companies transforming drug screening
Drug discovery starts with screening large libraries of small molecules to identify compounds (called hits) with activity against a biological target (examples of common classes of biological targets are proteins and nucleic acids).
This often involves searching and wandering around in a vast chemical space, comprising more than 10^60 molecules each waiting to be investigated by scientists (In case you need a comparison, there are something like 10^22 to 10^24 stars in our known Universe…).
Accordingly, during screening we can only compromise by focusing on the known chemical space, with chemicals that can be found on smaller chemical libraries, public databases (for example: PubChem, Chemspider, ZINC, NCI, ChemDB, BindingDB, ChemBank, ChEMBL, CTD, HMDB, SMPDB, DrugBank) and corporate collections—containing something like 10^8 molecules (100 million).
In modern drug discovery, the screening of large libraries can be done with in vitro high-throughput screening experiments of chemical libraries (High-throughput screening, HTS) and also with in silico methods to virtually screen compounds in order to identify novel drug candidates.
So, today’s newsletter is an overview of the different AI/ML tools, startups and companies transforming drug screening during hit identification (identify compounds/hits that have the potential to bind to a specific target protein or nucleic acid and modulate its activity in a desired way).
➡️ High-Throughput Screening (testing large libraries of chemicals on proteins, cells or animal embryos)
Synsight (France)
Synsight is a deep tech company developing a screening technology that enables the development of effective first-in-class drug candidates (for RNA targeting) based on an AI discovery platform and cell imaging, with phenotypic assays, high-content screening and high-content imager allowing to acquire more than 60000 images per day. In particular, Synsight developed the Microtubule Bench technology (MT bench®), an industrialised cell testing to screen molecules by microscopy and identify and quantify the modulations of small molecules on protein-protein interactions or between protein and nucleic acid. In 2022, Synsight secured a 1,5M funding round by a Chinese investor.
Ailynix
Ailynix is an AI Drug Design and Discovery company to predict the biological activity of chemical compounds. For example, Zunomics a subsidiary of Ailynix has developed a Computational Antiviral Drug Discovery (CAViDD) Platform to discover novel oral antiviral drugs, by mining unexplored chemical space to deliver innovative medicines.
Pangea Bio (Tel Aviv, Israel)
Pangea Bio utilises AI to uncover promising molecules from nature’s diverse chemical space, enhanced by traditional human evidence of safety and efficacy. In particular, The PangeAI discovery platform (Knowledge Graph, Computational Metabolomics, Compound Activity Profiling) accelerates the discovery and development of novel therapeutics from plants and other kingdoms of life, to translate nature’s metabolome into medicine, for neurological and neuropsychiatric diseases.
Vevo Therapeutics (San Francisco)
Vevo Therapeutics, a biotechnology company using its Mosaic in vivo drug discovery platform and AI models to uncover better drugs, was launched last year with an oversubscribed and upsised $12M seed financing round. The Mosaic platform is the first platform to make in vivo data generation scalable, with single-cell precision, while capturing patient diversity in drug response. In a single in vivo experiment, Mosaic can measure how a drug impacts cells from tens to hundreds of patients, generating millions of datapoints on drug-induced changes in gene expression.
Cellarity (Cambridge, Massachusetts)
Another AI drug discovery company utilising data at single-cell resolution to identify cell-state transitions that drive disease is Cellarity, a life sciences company founded by Flagship Pioneering. Until now, the dominant approach in drug discovery has been to reduce disease biology into a single molecular target and then leverage high-throughput screening to identify molecules that bind to these targets. But at Cellarity, they focus on the whole cell because, most often, a disease isn’t driven by one mechanism or protein, accordingly they use single-cell technologies to identify the cellular drivers of the transition from health to disease and then apply DL models to create drugs that reverse disease at the cellular level. On October, 2023, Cellarity announced a partnership with the Chan Zuckerberg Initiative to drive innovation in ML algorithms for single-cell analysis via support of the Open Problems in Single-Cell Analysis initiative.
“There are more than 1,500 algorithms developed for single-cell data, and understanding the deep complexity of cells captured by single-cell technologies requires robustly evaluating the performance of these methods.”
Diogo Camacho, Ph.D., Vice President of Computational Biology at Cellarity
Quris (Tel Aviv, Israel)
Quris, has an AI Chip-on-Chip platform (18 granted and pending patents) that allows automated testing of thousands of drugs on miniaturised Patients-on-a-Chip, while next-generation nano-sensors allow for continuous monitoring of the responses from each miniaturised organ to these drugs. Then, their ML classification algorithm is trained with the data continuously generated in this high-throughput system. Last month, Quris announced the extension of its collaboration with Merck to leverage Quris-AI platform’s ability to effectively identify liver toxicity risks in a selection of drug candidates.
A novel deep-learning algorithm called CeCILE (Cell Classification and In-vitro Lifecycle Evaluation), is used to detect and analyse cells on videos obtained from phase-contrast microscopy, up to a sample size of 100 cells per image frame, in order to gain information about cell numbers, cell divisions and cell deaths over time during drug screening.
In this GEN webinar (Register here), Dr. Shelby Wyatt VP of Global Pharma Strategy at Flywheel.io (a cloud-based company with a medical imaging AI platform) will discuss how AI-based medical imaging helps imaging/data scientists accelerate drug development initiatives and how Flywheel builds reliable, scalable medical imaging solutions that seamlessly integrate advanced AI technology from NVIDIA and other technology partners to enable robust imaging data management and analysis. During drug screening Flywheel can offer the following solutions to the imaging research labs:
Metadata management with search
Automated pre-processing & pipelines
Machine learning workflow
Customisation via APIs, Python, & Matlab
Provenance
BIDS support, and
Secure collaboration.
Flywheel (Minneapolis, Minnesota)
On June 27, 2023, Flywheel announced it has raised $54M in Series D funding co-led by Novalis LifeSciences LLC and NVentures, NVIDIA’s venture capital arm. Microsoft also participated in the round, along with insiders Invenshure, 8VC, Beringea, Hewlett Packard Enterprise, Intuitive Ventures, iSelect, Gundersen Health System, Seraph, and Great North Ventures. Faegre Drinker Biddle & Reath LLP served as counsel to Flywheel in connection with the financing.
Molecular Devices
Molecular Devices, one of the leading providers of high-performance bioanalytical measurement solutions for life science research, pharmaceutical and biotherapeutic development, has just introduced the CellXpress.ai™ an automated cell culture system for screening, a revolutionary ML-assisted solution that standardises the entire cell culture journey to deliver consistent, unbiased, and biologically relevant results at scale. The CellXpress.ai™ is an AI-driven cell culture innovation hub that gives your team total control over demanding cell culture feeding and passaging schedules—eliminating time in the lab while maintaining a 24/7 schedule for growing and scaling multiple stem cell lines, spheroids or organoids. All of it backed with the assurance of a full event log to confirm on-time feedings and critical task execution with complete digital microscopy records.
Moreover, Molecular Devices is offering
an AI-based software that provides Photoshop-like tools for image annotation, and
the ImageXpress® Confocal HT.ai High-Content Imaging System, designed to help researchers advance phenotypic screening of 3D organoid models. The ImageXpress® utilises a seven-channel laser light source with eight imaging channels to enable highly multiplexed assays while maintaining high throughput by using shortened exposure times. Water immersion objectives improve image resolution and minimise aberrations so scientists can see deeper into thick samples. Moreover, the combination of MetaXpress® software and IN Carta® software simplifies workflows for advanced phenotypic classification and 3D image analysis with ML capabilities and an intuitive user interface.
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◼ Using AI 🤖 to improve efficiency and effectiveness during clinical trials
➡️ Assay Development and assay interference during screening
Hit Dexter
A different problem during screening is assay interference caused by small molecules during in vitro experimenets. Several approaches have been developed that allow scientists to flag potentially “badly behaving compounds”. Usually, these compounds are typically aggregators, reactive compounds and/or pan-assay interference compounds and many are frequent hitters. The solution to this problem comes from Hit Dexter, a recently introduced ML approach that predicts how likely a small molecule is to trigger a false positive response in biochemical assays. In particular, the new Hit Dexter 2.0 web service covers both primary and secondary screening assays, providing user-friendly access to similarity-based methods for the prediction of aggregators and dark chemical matter (a set of drug-like compounds that has never shown bioactivity despite being extensively assayed), as well as a comprehensive collection of available rule sets for flagging frequent “bad” hitters and compounds including undesired substructures.
➡️ Protein prediction tools (AI can assist in structure-based drug discovery by predicting the 3D protein structure, that determines the biological function of the target)
AlphaFold
AlphaFold is considered the gold standard for AI protein structure predictions, making predictions with high accuracy of something like 200 million proteins. Other 3D protein prediction tools are Meta’s ESM Metagenomics atlas, ColabFold, RoseTTAFold, IntFOLD and OmegaFold launched by Helixon.
In particular, RoseTTAFold is a “three-track” neural network that simultaneously considers patterns in the protein sequence, how a protein’s amino acids interact with one another and a protein’s possible 3D structure. In this neural network, sequence, distance and the 3D information flow back and forth, allowing the network to collectively reason about the relationship between a protein’s chemical parts and its folded structure.
RoseTTAFold can solve challenging x-ray crystallography and cryo–electron microscopy modeling problems and is the most renowned tool used for ab initio protein predictions, that are the most challenging to conduct, as they involve predicting protein structures based only on first-principles and without using existing templates.
Both AlphaFold2 and RoseTTAFold rely on Multiple Sequence Alignments (MSAs) as inputs to their models, which map the evolutionary relationship between corresponding residues of genetically-related sequences, derived from large, public, genome-wide gene sequencing databases that have grown exponentially since the emergence of next-generation sequencing. Finally, the Rosetta molecular modeling software package is used right now by OutpaceBio, to customise drug design creating next-generation smart cell therapies.
Helixon (Beijing, China)
On July 20, 2022, the Chinese biotech firm Helixon launched OmegaFold, a new combination of a protein language model (PLM) that allows making predictions from 1) single sequences, eliminating the need for MSAs, and 2) from a geometry-inspired transformer model, the Geoformer module, a new geometry-inspired transformer neural network to further distill the structural and physical pairwise relationships between amino acids (study).
So far, OmegaFold claims to outperform RoseTTAFold and achieved similar prediction accuracy to AlphaFold, along with other models such as HelixFold-Single and ESMFold. It is touted to have a higher potential in predicting the structure of orphan proteins and antibodies that don’t require MSAs as their input.
Evozyne
NVIDIA and Evozyne created a generative AI model for proteins, to generate predictions for proteins whose structure is unknown. Evozyne used NVIDIA’s implementation of ProtT5, a transformer model that’s part of NVIDIA’s BioNeMo, a software framework and service for creating AI models for healthcare, and created two proteins with significant potential in healthcare and clean energy. On September 27, 2023, Evozyne announced the closing of an $81M Series B investment round. Fidelity Management & Research Company and OrbiMed led the funding with participation from NVentures, NVIDIA’s venture capital arm. Previous investors Paragon Biosciences and Valor Equity Partners expanded their support in the round.
➡️ AI-Driven Virtual Screening (Virtual screening (VS) is the computational counterpart of the experimental HTS, in which compounds from chemical libraries are tested for their activities against a biomolecular target that might have therapeutic relevance toward a specific disease)
Deep Docking Platform
The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, synchronised with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule–sized chemical libraries without using extraordinary computational resources.
Codexis
Codexis is a leading enzyme engineering company that has a proprietary platform, the CodeEvolver that provides in silico, high-throughput assay screening with AI, and has the power to transform the performance of an enzyme, tailoring it for a specific application and process. By using powerful ML tools and sophisticated molecular, cellular and bioanalytical workflows, at Codexis they can design and screen libraries of thousands of enzyme variants in high throughput, then sequence every variant and correlate its sequence with its performance in a highly application-relevant screen. Among Codexis’ partners you can find Merck, GSK, Novartis, Nestle, Takeda and many more.
Adimab
Adimab is the industry leader in translating your target hypotheses into therapeutically relevant antibody drugs. They have a traditional discovery process that starts with AI mining their large synthetic human IgG repertoire, which ensures every antibody delivered is unique.
On February 09, 2023, Ablexis—a biopharmaceutical company focused on licensing its AlivaMab Mouse technology for antibody drug discovery— announced a license agreement with Adimab in order to implement Ablexis’s AlivaMab Mouse into Adimab’s proprietary yeast-based technology for antibody drug discovery. Financial terms of the license were not disclosed. And just a month ago, Ono Pharmaceutical (one of the largest pharmaceutical companies in Japan) and Adimab have signed a drug discovery collaboration agreement for the development of antibody drugs in the oncology segment.
Innophore
Innophore with a cutting-edge drug and enzyme discovery platform that uses AI guided point-cloud technology, not only analyses a protein’s 3D structure but includes extended surface properties (HALOS) and volumetric cavities (catalophores) to predict target’s characteristics and reactivity in AI virtual screening simulations. Their AI-driven strategy to design novel therapeutic enzymes combines the Catalophore technology, a mix of prepared protein structural data (CATALObase), and search 🔎 algorithms and patterns tailored to specific needs.
Among their products you can find also Cavitomix a PyMol Plugin. CavitOmiX plugin for Schrodinger’s PyMOL, is a tool that allows you to analyse protein cavities from any input structure. You can dive deep into proteins, Catalophore cavities and binding sites using crystal structures and state-of-the-art AI models from OpenFold (powered by NVIDIA’s BioNeMo service), DeepMind`s AlphaFold and ESMFold by Meta. And by just entering any protein sequence users can get the structure predicted by OpenFold or ESMFold loaded into their PyMOL within seconds.
Gandeeva
Gandeeva’s technology includes three proprietary platform modules working in concert: 1) SPOTLIGH, a Target Selection Engine, an AI-based approach to identify a continuous stream of validated targets, 2) HYPERFOCUSTM, a Cryo-EM Engine, a state-of-the-art atomic resolution imaging to map druggable sites and 3) CRYO-CADDTM, a Drug Discovery Engine, a rapid iterative cycle to generate structural insights at the speed of chemistry.
On March 30, 2023, Gandeeva Therapeutics announced today the initiation of a research collaboration with Moderna Inc. to explore applications of Gandeeva’s technology platform for a Moderna program.
Until next time,
📄 References:
Exploring Data Driven Drug Discovery
Drug screening and drug design with AI/ML
Editor’s Note
Specialized Newsletters like MetaphysicalCells are quick ways to gain access to the frontiers of biotechnology and A.I. Not all of us have the academic background to easily follow so subscribing to domain experts like Marina can truly help. By understanding the startup ecosystem we can also prepare ourselves to be potential investors when and if the companies go public.
The base use case of A.I. Supremacy is to be more of a generalist Newsletter that can lead to knowledge in multiple areas. We could not do this without our peers, partners and guest contributors. I’m also seeking AI startups to tell their origin story and you can DM me on Linkedin.
A lot of the startups mentioned are early-stage, or even pre Series A companies that most people would have never heard of. There is active speculation on genomics startups, however the drug discovery A.I. startups are a whole different breed. A.I. driving an era towards a biotech golden age is just years in the future. Few fields have so much to gain via Generative A.I. as does healthcare, and biotechnology holds a lot of promise that is likely to be unlocked in the 2023 to 2043 period. While longevity startups get a lot of the hype, a variety of biotech startups are accelerating new trends incorporating A.I. with improved efficacy.
Further reading:
AI-powered drug discovery is the future.
Read More in AI Supremacy
