AB Research & Media

I'm helping C-level executives and investors, as well as B2B companies in life sciences to stand out from the noise. 

In just one year, I've successfully delivered dozens of content projects for various clients, including life science-focused venture capital funds, a number of clinical stage publicly traded companies, and various chemical and biotech startups and larger organizations. 

Reach out here or via www.AndriiBuvailo.com to schedule a 30 min exploratory call.

Science & Tech Communicator, Ghostwriter

BiopharmaTrend.com

Your go-to resource for news, trends, and analysis of the cutting-edge advances in pharma, biotech and healthcare.

Reach out for sponsorship opportunities: info@biopharmatrend.com

Co-Founder, Advisor 

Enamine Ltd.

Head Of Digital Marketing

Together with our great team we conceptualized and developed a major part of www.EnamineStore.com, the world's largest e-shop of fine chemicals for drug discovery, and implemented a number of digital marketing initiatives. 

Over several years, we managed to grow this channel from insignificant project in the overall structure of the company's global revenue to a strategically significant % (online + buyer-vendor integrations). 

I also supervised punch-out and API integrations with most strategic buyers and smaller customers. 

Finally, under my supervision, we created a small department of digital marketing, which I then headed. We built Enamine's LinkedIn presence from ground-up to 13k followers and I was directing the major update of the company main website www.enamine.net from old "business-card" version into a multifunctional digital asset.

Director Of Ecommerce

Digital Marketing Manager

Forbes Technology Council

Published 3 articles and 10 small thought leadership contributions on Forbes.com.

Contributing Writer

YUNASKO

YUNASKO-Ukraine is an R&D company developing technology for producing highly powerful ultracapacitors. During its life, the company manufactured prototypes of the most powerful ultracapacitors on the market, elaborated design solutions for such prototypes and grew intellectual property portfolio, which included several Ukrainian, European, French, Latvian, USA and international patent applications.

Director

Project Management and Administration
Intellectual Property Management
Supercapacitor Research
Research Consultancy (Chemistry and Material Science)

Research Engineer / Intellectual Property Coordinator

Temple University

I spent a year in The Borguet Group at Temple University. Responsibilities included research work in the field of gas sensor technology research and development; supervising research work by undergraduate students, preparation of research papers for publication; preparation of presentations in PowerPoint, giving talks on the subject at conferences and research meetings; establishment of collaborative connections.

Research Scholar 

#1 Most Followed Voice in AI Business (1.5M) | Leading AI Entrepreneur & Advisor | Former Amazon, IBM | LinkedIn Top Voice | @alliekmiller on Instagram, X, TikTok

Former scientist. Tech and business writer/editor, published on Forbes.

Co-founder @ www.BiopharmaTrend.com. 

IT management experience, B2B e-commerce supervision in a global chemical company 
Executive and entrepreneurial experience managing startups and consulting agencies. Gen AI and prompt engineering expert. 

A former researcher used to work in Ukraine/United States/Germany/Belgium (coordination/bioinorganic chemistry; nanotech; sensors and biosensors, materials and surface science). PhD in Physical Chemistry. 

Personal website: https://www.andriibuvailo.com

NEWS: Anthropic launches foundational AI model for life sciences! 🧬

The company has launched Claude for Life Sciences, a version of its AI assistant built specifically for research and development teams in biotech, pharma, and academia.

It’s based on their existing Claude models (including the recently updated Claude Sonnet 4.5, which they say is much better at life sciences tasks), but with some key additions: it connects to tools researchers already use, like Benchling, PubMed, 10x Genomics, and Synapse, and is designed to support the full research workflow.

The idea is to help scientists with everything from digging through papers and forming hypotheses to analyzing lab data and writing up regulatory submissions.

A few important points:

⚙️ Tool integrations: Claude now connects with major platforms used in life sciences, so researchers can pull in data, compare experiments, and generate summaries — all in context.

⚙️ Workflow coverage: Instead of just helping with one-off tasks, it’s built to support end-to-end work — for example, writing a study report straight from lab data that could go into a regulatory submission.

⚙️ Partnerships for rollout: Anthropic is working with KPMG, Deloitte, Caylent, and cloud providers like AWS and Google Cloud to help life sciences companies actually adopt and implement it.

💡 That being said, they’re being clear that AI won’t magically speed up things like clinical trials, but it can cut down the time researchers spend on tedious work that used to take days.

Did you happen to play with the model? Share your experience in the comments 👇 . 

Image credit: Anthropic

Andrii Buvailo, Ph.D.

Science & Tech Communicator | AI & Digital | Life Sciences | Chemistry

These 17 companies are pioneering AI foundation models for proteins, pathology, brainwaves, cell images, small molecules, and more! 🚀 

⚙️ Just to remind, foundation models in biology are large, pre-trained neural networks, often transformers, trained on massive, unlabeled datasets across modalities (e.g., sequences, structures, images, text, etc.) to learn general representations that can be fine-tuned or prompted for diverse downstream tasks via transfer learning, enabling cross-domain generalization in genomics, proteomics, phenotyping, and drug discovery.

☕ Last year I compiled a list of companies pioneering foundation models in bio for BioPharmaTrend.com, but since then, a lot happened, so time for update (new article in the comments). 

Just to highlight some 2025 developments for some of the companies in the list: 

Retro Biosciences is now starting an Alzheimer’s reversal trial rooted in reprogramming, powered by transcription factors co-designed with OpenAI’s GPT‑4b model. 

Insilico Medicine’s Precious3GPT suuite of models has extended its aging simulation framework to fibrosis, while their AI-designed drug Rentosertib reached proof-of-concept and made it into Nature Medicine.

Recursion launched Phenom-Beta on NVIDIA’s BioNeMo platform — a model trained on millions of microscopy images to create general-purpose embeddings of cell phenotypes. 

Tempus AI, after acquiring Paige, released PRISM2, a foundation model trained jointly on pathology slides and clinical dialogue. 

BioMap quietly scaled its xTrimo model to 210 billion parameters, spanning DNA, RNA, proteins, and cells.

Chai Discovery released Chai-2, a generative antibody model with one of the highest hit rates seen to date (as they claim). 

NOETIK pushed forward with its OCTO-Virtual Cell model, trained on spatial data and CRISPR perturbations to simulate how tumors respond to different interventions. 

Enveda continues to scale its mass-spec-based foundation model, PRISM, trained on over a billion spectra, while Helical launched Helix-mRNA, a compact foundation model for mRNA design, now live in the AWS Marketplace.

The list keeps growing. Terray Therapeutics is scaling its generative chemistry engine, tNova. Ginkgo Bioworks, Inc. launched its Ginkgo Technology Network, linking foundation models to real-world workflows in synthetic biology. 

MindWalk Corp. (formerly ImmunoPrecise Antibodies) advanced its explainable LensAI platform into aging and GLP-1 biology. 

Piramidal is testing an electroencephalogram-focused model in Cleveland Clinic ICUs. Atomic AI released ATOM-1 for RNA design. Bioptimus launched H-Optimus-0 for pathology... and on and on. 

If you are interested in details and some funding rounds in this space, make sure to read the article. 

And of course, the team behind Where Tech Meets Bio newsletter regularly covers this topic in depth, so subscribe on Substack too... 

Image credit: BiopharmaTrend

NEWS: Sanofi signed a 3-year global license agreement to use BenchSci’s gen AI platform ASCEND™ across its preclinical research teams 📊 

At a glance, ASCEND is an AI copilot for biomedical research that is supposed to help scientists get structured, evidence-backed answers to complex biological questions.

I think the core problem this tries to address is the volume of biomedical data, which makes it increasingly difficult to process new informatoin and work with it. 

According to a case study published on BioPharmaTrend.com (link in the comments 👇), searching for something like “KRAS” , for instance, returns 30,000+ papers in PubMed, many with conflicting results, buried methods, and inconsistent terminology. Internal data at large pharma is also often siloed, unindexed, and usually not reusable across teams.

Most AI tools in this space are built around search or summarization, like frontier LLM-based tools. 

BenchSci claims ASCEND addresses this by organizing the world’s biomedical evidence into a Biological Evidence Knowledge Graph:

⚙️  400M+ entities, e.g., genes, proteins, diseases, phenotypes, reagents, pathways, model systems

⚙️ 1B+ relationships, each grounded in specific experiments from full-text literature, figures, reagent catalogs, patents, and internal datasets

⚙️ Backed by a curated ontological layer that standardizes concepts across sources like UniProt, KEGG, HPA, and commercial reagent metadata

So, this allows scientists to ask complex, biologically meaningful questions like:

❓ “What immune evasion markers co-occur with KRAS mutations in pancreatic cancer models?” 

Instead of returning articles, ASCEND would return structured, evidence-backed results, showing how markers, models, reagents, and outcomes connect, with direct links to source data.

Why it matters? 

👉 I think by enabling structured queries over biological systems, not just text, ASCEND could potentially be useful for scientists to triage targets, design potentially better experiments, and align internal research with the broader scientific landscape. This is very much in line with the idea of our report "Beyond Legacy Tools: Defining Modern AI Drug Discovery for 2025 and Beyond" I co-authored earlier this year (link in the comments 👇 ).

I think every big pharma organization is now trying to make better use of their data, and Sanofi’s deployment of ASCEND reflects a growing need for not just faster access to information, but a shared, structured foundation for how biology is understood across teams.

Anyway, let me know what you think and subscribe to Where Tech Meets Bio! 🙏 ☕ 

Image credit: BenchSci

Inside Big Pharma's AI playbook 🤖 💰 

We hear a lot about trending AI drug discovery companies of the new generation, e.g., Insilico Medicine, Recursion, Cradle, Generate:Biomedicines, and dozens of emerging platforms. 

But what about the largest and most established pharma organizations, the likes of Eli Lilly and Company, AstraZeneca, or Sanofi? How do they deal with the growing pressure to adopt data-first strategies and relevant technologies?

Let's have a brief look at the latest developments ☕ 👇

View newsletter: Where Technology Meets Biology

We hear a lot about trending AI drug discovery companies of the new generation, e.g., Insilico Medicine , Recursion, Cradle, Generate:Biomedicines, an

Open article: Inside Big Pharma's AI Playbook by Andrii Buvailo, Ph.D. • 3 min read

The microbiome may be oncology’s next precision frontier, according Dr. Rafik FELLAGUE-CHEBRA, MD, MSc, Medical Director, Novartis Oncology 📈   

Data show antibiotics can cut immunotherapy efficacy by up to 50%, while engineered bacteria and fecal transplants from responders can restore therapeutic response. Microbial metabolites also interact with drug targets like mTOR and AKT, altering treatment outcomes.

With potential to cut trial timelines by 40–60% and tap into a $280B market, the oncobiome isn’t just fancy science.... it may be the next strategy! 🚀 

Anyway, as I am wrapping up my participatin in this years Drug Discovery Innovation Programme (DDIP) 2025, here in sunny Barcelona, just wanted to share brief highlights I particularly liked, as well as other weekly news 👇 

Let me know what you think!

Hey there! This week I am reporting from the Drug Discovery Innovation Programme (DDIP) , from a sunny Barcelona. Just wanted to share quick highlight

Open article: The "Oncobiome": Mapping a New Cancer Treatment Paradigm (And Other Insights from DDIP2025 in Barcelona) by Andrii Buvailo, Ph.D. • 4 min read

The "Oncobiome": Mapping a New Cancer Treatment Paradigm (And Other Insights from DDIP2025 in Barcelona)

NEWS: Nanyang Biologics, a Singapore-based AI drug discovery company, to go list on Nasdaq via a US$1.5 billion SPAC deal. 

Nanyang develops new medicines by combining natural compound libraries with artificial intelligence. Its approach has three main components:

Natural compound collection: over 50,000 organisms are being catalogued, building one of the world’s largest libraries of plant, fungal, and microbial molecules as per company's website.

AI prediction model (DTIGN): a graph neural network that integrates molecular structures and protein language models to predict how compounds interact with disease targets. Tests are said to show it improves prediction accuracy and reduces screening time compared to traditional methods.

Biomedical validation: computational predictions are confirmed through laboratory studies, including safety and efficacy testing.

The company has partnered with Nvidia, Hewlett Packard Enterprise, and Equinix to support large-scale computing and secure digital infrastructure. 

Its lead candidate, NB-A002, is designed to treat cancers linked to DNA damage repair deficiencies and resistance to current therapies.

I think it is especially interesting because of its focus on synthetic lethality and DNA damage repair cancers, a frontier with high therapeutic and commercial potential.

My coverage for BioPharmaTrend.com in the comments below. 👇 

Image credit: Nanyang Biologics

📈 In 2025, Google, NVIDIA, Microsoft, and almost every other "tech giant" made headlines in life sciences, here is a timeline:  

📅 February: Amazon Web Services and Memorial Sloan Kettering Cancer Center began a collaboration focused on early-stage drug discovery. The project integrates Cryo-EM, Drug Discovery Workbench, and SageMaker to support structure prediction and compound screening.

Apple and Brigham and Women’s Hospital launched a virtual health study. Participants use iPhone, Apple Watch, AirPods, and other devices to track cognition, cardiovascular function, sleep, and hearing.


📅 March: Google introduced TxGemma, a set of open AI models fine-tuned from Gemma-2. These models were evaluated on 66 therapeutic prediction tasks across small molecules and proteins, matching or outperforming specialist baselines.

📅 May: Microsoft and OpenAI presented Copilot for Healthcare, combining structured data access, ambient listening, and LLM-based documentation tools. This system integrates with clinical environments and supports imaging, ECG interpretation, and patient summarization.

📅 June: Microsoft published results on MAI-DxO, a diagnostic orchestration system. On 304 NEJM benchmark cases, the model reached 85.5% accuracy in sequential diagnosis. Physician accuracy in the same benchmark: 20%. MAI-DxO also achieved lower diagnostic cost by reducing unnecessary tests.

📅 Probably the most active player, NVIDIA, continuously pushes its solutions across biomedical use cases. The company’s Parabricks toolkit is used for genomics pipelines; its BioNeMo platform underpins generative biology efforts at companies such as Genentech and Recursion. 

At the Francis Crick Institute, NVIDIA GPUs enabled 26× acceleration in genomics workflows.

What is clear, each deployment extends AI from model to workflow. I think the focus shifts from just algorithmic novelty to infrastructure design: compute layers, data pipelines, and system-level integration.

This is tiny part of the insight from the latest deep-dive by Where Tech Meets Bio editorial team. Check it out, link in the comments 👇

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