Digital biomarkers are turning everyday movement into clinically useful data, giving doctors a clearer picture of what’s happening between appointments, and giving pharma new ways to measure drug impact earlier and more precisely. In this episode, Dr Andree Bates interviews Dr Quique Llaudet, CEO and co-founder of Ephion Health, about precision monitoring and how AI-driven mobility analysis is changing both clinical care and drug development.

Quique shares his journey from academic research into entrepreneurship, driven by a desire to turn science into real products that help patients. Ephion Health grew out of early work with paediatric hospitals in Barcelona, where sensor technology used in rehabilitation and exoskeleton projects revealed a bigger opportunity: objective, high-sensitivity gait and movement analysis that can detect disease signatures and track progression over time.

The conversation breaks down what a digital biomarker actually is: a measurable signal of health captured via connected devices and analysed with digital methods. Ephion’s platform integrates multiple validated, off-the-shelf sensors to capture rich movement data in a short test, replacing blunt measures like the six-minute walk test with something both more sensitive and less stressful for patients. The system then combines key parameters into a single composite score to track progression and treatment response.

Quique also tackles the “black box” concern head on. He explains how their models are developed alongside clinicians, with clinical relevance checked throughout, and how doctors can inspect the underlying parameters behind the biomarker score in a dashboard. For rare diseases with limited data, he highlights deep collaboration with clinicians and patient associations, and the use of synthetic data to support modelling and testing.

Finally, Quique outlines the economics: reducing specialist assessment time, enabling more frequent remote monitoring, supporting earlier treatment adjustments, and helping pharma generate evidence in real-world settings. The long-term vision is continuous monitoring that helps clinicians act earlier, plus AI-assisted diagnosis and eventually prevention.

Topics Covered

• What digital biomarkers are and how they differ from traditional biomarkers

• Turning mobility data into clinically meaningful signals

• Multi-sensor monitoring: IMUs, pressure insoles, and EMG

• Why short tests can beat the six-minute walk test

• Composite biomarker scoring and tracking treatment response

• AI patterns clinicians may sense but cannot quantify

• Explainability and building models “hand in hand” with doctors

• Data challenges in rare disease and the role of patient associations

• Synthetic data for modelling and validation

• Economic impact: time savings, remote monitoring, and better treatment adjustment

• Pharma use cases: real-world evidence and earlier efficacy signals in trials

About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

Digital twins have become one of the most promising tools in Alzheimer’s research, but the bigger story is what happens when they scale across disease areas. In this episode, Dr Andree Bates interviews Aaron Smith, Founder and Head of Machine Learning at Unlearn AI, about how “digital twin generators” can transform trial design by modelling realistic patient progression and improving statistical power without compromising the fundamentals of randomised controlled trials.

Aaron shares his journey from academic mathematics into computer vision and machine learning, then into biopharma, where Unlearn began by building generative models that learn the joint distribution of clinical variables. In practice, that means the model can take baseline patient measurements and generate likely future progressions that are as indistinguishable from real clinical records as possible.

The conversation dives into a key misconception: digital twins are not only about replacing control arms. Aaron explains a regulatory friendly approach where you keep standard trial structure, but add counterfactual information for every patient into the analysis. Unlearn’s best known method, ProCOVA (prognostic covariate adjustment), summarises a predicted control outcome per patient and uses it for covariate adjustment, creating more efficient treatment effect estimates. The headline result is simple: you can increase power, or reduce recruitment burden while maintaining power, potentially speeding time to results.

Finally, Aaron explains why scaling across diseases is genuinely hard. Data structures differ wildly by indication, missingness can block transfer learning, and areas like oncology require modelling complex treatment histories. He also highlights that combining sources is not just “more data”, it demands careful harmonisation and context modelling to avoid biased predictions, especially when bringing in real world evidence.

Topics Covered

• What “digital twin generators” are in clinical trials

• Generative modelling of clinical records and disease progression

• Counterfactual prediction under standard of care

• Why replacing control arms is not the only use case

• ProCOVA and prognostic covariate adjustment

• Getting more statistical power and reducing trial size

• FDA openness to digital twins in trials and what it enables

• Why scaling across disease areas is not just parameter tuning

• Missing data, confounding context, and data harmonisation

• CNS versus oncology modelling challenges

• Real world evidence and how to validate digital twin models

About the Podcast

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

AI in drug development is moving beyond “failure prevention” into something much bigger: redesigning how we discover, develop, and deliver medicines. In this episode, Dr Andree Bates speaks with Vitalay Fomin of Numenos about biomarker discovery, patient stratification, and why the next breakthroughs come from breaking down data silos across diseases, modalities, and even species.

Vitalay shares his background across biotech and pharma, including work in biomarker discovery, translational medicine, and data science, and how frustration with existing approaches led her to build a new architecture for clinical genomic insights. A core theme is that traditional methods often oversimplify biology by forcing outcomes into binary labels and treating each disease area as an isolated box, even when the available data is too limited to answer meaningful questions well.

The conversation explores how foundation model approaches can unify clinical, genomic, transcriptomic, proteomic and imaging signals to create a fuller “biological fingerprint” of each patient. Vitalay explains how this can enable earlier insight from single-arm trials by effectively benchmarking against standard-of-care cohorts, helping teams enrich later-stage trials with the right subpopulations sooner, and reducing time and cost.

They also discuss the real blockers to adoption: not only scientific conservatism, but commercial uncertainty around how Big Pharma structures deals with tech-bio companies that bring platforms rather than single assets. Vitalay argues that explainability is non-negotiable in this space, because clinicians, scientists, patients, and regulators will not trust black-box predictions.

Topics Covered

• Why AI is shifting from failure prevention to pipeline redesign

• Biomarker discovery beyond binary responder vs non-responder labels

• Breaking disease silos to learn across indications

• Multimodal integration: DNA, RNA, protein, imaging, and clinical data

• Using foundation models to bridge trial data and real-world data

• Patient stratification and trial enrichment from early studies

• Reverse translation and identifying unmet need before target hunting

• Explainability, trust, and regulatory readiness

• Adoption barriers: culture, champions, and deal structures for tech-bio

• Misconceptions about AI in drug development and why “press a button” is a myth

About the Podcast

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

Enterprise AI is shifting from experiments to infrastructure, and that changes everything. In this episode, Dr Andree Bates interviews Jocelyn Houle, Senior Director of Product Management at Securiti.ai, to explore the future of enterprise AI, agents, automation, and the single biggest blocker to scale: trust.

Jocelyn shares what she is seeing across highly regulated industries as organisations move beyond proof of concept into production. She explains why the agent era raises the stakes: when you add LLMs into workflows, systems become non-deterministic and harder to trace end to end. In her words, once the data goes in, you cannot easily untangle it, so organisations need stronger controls around permissions, auditing, and policy enforcement.

A major theme is that data foundations matter more than ever. Jocelyn warns that agents will not magically repair messy data, they will expose weak data quality immediately. From there, she outlines how trust can be won or lost at the prompt layer, both outbound (what the model says to customers) and inbound (what users share with the organisation). She also discusses “toxic combinations”, where overlapping access can accidentally leak sensitive information, plus the growing need for prompt screening and tracking to reduce risk.

The conversation also digs into explainability and auditability, with Jocelyn being refreshingly honest that the perfect solution is not here yet. Instead, enterprises are using practical approaches like benchmarking releases side by side, cataloguing AI agents in use, and building governance that is starting to look more like modern cybersecurity: baked in from the start, not added as an afterthought.

Jocelyn closes with clear advice for leaders: start “left” with raw data controls, build a truly cross-functional team, and begin setting up auditability even with imperfect tools, because regulators are catching up and they will expect responsible behaviour.

Topics Covered

• Where enterprise AI adoption really stands today

• What makes AI agents different from traditional automation

• Non-determinism, traceability, and why permissions matter

• Data mapping, policy controls, and reducing sensitive data leakage

• Prompt security: outbound and inbound trust risks

• “Toxic combinations” and exposure through agent workflows

• Explainability, benchmarking, and parallel release testing

• AI governance becoming as essential as cybersecurity

• Top 3 pieces of advice for CTOs starting their AI journey

• Why enterprise AI will become so embedded we stop talking about it

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

About the Podcast

Clinical trials are a massive industry with brutal economics, long timelines, and failure rates that would be unacceptable in almost any other sector. In this episode, Dr Andree Bates is joined by Dr Joseph Geraci of NetraMark to break down why trials fail so often, how patient heterogeneity drives cost and uncertainty, and where AI can realistically shift the economics.

Joseph shares his unusual path from mathematics and mathematical physics into oncology and medical science, including a decision to move into hospital research rather than follow a more traditional academic route. That shift shaped his focus: not just discovering more molecules, but understanding why the same drug can work brilliantly for some patients and fail for others.

A central theme is that clinical trials are not “one disease, one patient type”. In many areas, disease definitions are too broad for trial design, making trials feel like trying to hit multiple dartboards with one dart. Joseph explains how NetraMark’s approach aims to identify meaningful subpopulations inside small datasets, finding the “pocket” where a drug’s true advantage shows up, without discarding patients as outliers.

The conversation also touches on regulators, including growing interest in innovation pathways, but also the fear pharma teams have about changing protocols and risking setbacks. Joseph argues that AI’s biggest economic value in trials is speed, using better insight from limited trial data to guide enrichment strategies, smarter substudy decisions, and faster iteration, especially in oncology and rare disease where time is everything.

Topics Covered

• Why clinical trial economics are becoming unsustainable

• Patient heterogeneity and why disease definitions break trials

• Finding “pockets” of responders within small datasets

• Trial enrichment and substudies that reveal a drug’s advantage

• Why pharma adoption can be slow, even when failures are constant

• Regulatory interest, guidelines, and sponsor risk aversion

• Large language models vs mathematically augmented AI approaches

• Speed as the biggest economic lever in trials

• Practical examples across depression, schizophrenia, oncology, and beyond

• What clinical trials could look like in five years with AI-driven insight

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more. 

Dr. Andree Bates LinkedIn | Facebook | X

AI capability has never been higher, yet most pharma AI programmes are still failing to create measurable business impact. In this solo episode, Dr Andree Bates breaks down why many pharma and biotech AI strategies are “broken before they even begin” and what a real AI strategic blueprint needs to include if you want adoption, scale, and outcomes, not just impressive pilots.

Dr Andree explains the core paradox: AI can now synthesise literature at speed, accelerate discovery, and outperform human experts in specific tasks, but the business results are often disappointing because the failure is rarely technical. It is strategic. She describes the “technical obsession trap”, where organisations spend months optimising models and benchmarking competitors while adoption remains low and teams are not operationally ready to act on the outputs.

She outlines three common failure modes:

• Innovation Theatre, where disconnected pilots never compound into enterprise value

• Competitor benchmarking, where companies copy use cases that do not fit their context

• Technology first strategy, where tools are bought before priorities are defined
  

From there, she maps what a strong pharma AI blueprint must cover: grounding in business objectives, end to end deployment architecture (data, governance, capability, change), leadership and culture, rigorous financial modelling tied to revenue and ROI, and alignment across functions including commercial, medical, regulatory, R&D, market access, insights, and tech teams.

Dr Andree closes with a clear challenge for leadership: competitive advantage will come to organisations that build the most intelligent operating model around AI, not those with the biggest budgets. She also offers a 45 minute AI strategic diagnostic for pharma and biotech leaders who want an honest read on what to fix before investing further.

Topics Covered

• Why pharma AI impact often disappoints despite powerful tools

• The “technical obsession trap” and the AI strategy blind spot

• Innovation Theatre, competitor benchmarking, and technology first mistakes

• What a pharma AI strategic blueprint must include

• Governance as a foundation for scale and regulatory trust

• Leadership, culture, and adoption as the real differentiators

• Financial modelling and prioritisation based on ROI and revenue impact

• Organisational alignment across the full pharma value chain

• Choosing the right advisory partner and avoiding generic frameworks

• Why strategy must come before technology to build durable advantage
  

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

Healthcare accessibility is still being held hostage by phone queues, missed calls, and clunky portals. In this episode, Dr Andree Bates sits down with Josh Taylor of TxtSquad to explore why simple, secure texting can remove friction for patients and providers, and how AI can support the conversation without making care feel robotic.

Josh explains why SMS works where apps often fail: it is familiar, low effort, and asynchronous. That means patients can reply on their own time, while care teams can manage conversations in a way that fits real clinic workflows instead of forcing everyone into yet another platform.

They dig into the pharma and adherence angle too. Josh shares how two-way messaging helps teams move beyond generic reminders and uncover the real reasons people stop taking medication, opening the door to earlier support, smarter interventions, and better outcomes.

Finally, Josh outlines where AI adds value inside messaging: triage for common questions, translation, summarisation, and staff copilots. He also highlights the importance of compliance, clear boundaries, and human oversight when dealing with sensitive health information. The future, he suggests, is smarter communication across SMS, voice, and phone-based assistants, designed to meet patients where they already are.

Topics Covered

• Why portals and apps struggle with adoption in healthcare

• SMS as a low-barrier, asynchronous engagement channel

• Building a two-way patient to provider communication loop

• Medication adherence: understanding drop-off, not just sending reminders

• Patient assistance programmes and support at scale

• Clinical trial recruitment and retention using messaging

• AI in messaging: triage, translation, summarisation, and staff copilots

• Compliance considerations and handling sensitive information

• What comes next: voice agents and deeper integration with phone workflows

About the Podcast

AI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.

This show brings together leading experts and changemakers to demystify AI and show how it’s being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI’s role in social good, this podcast offers valuable insights.

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget.

As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses.

This podcast features many experts who have developed powerful AI powered tools that are the secret behind some time saving and supercharged revenue generating business

Dr. Andree Bates LinkedIn | Facebook | X

As artificial intelligence becomes increasingly woven into everyday life, the real question isn’t just what AI can do — it’s what it should do for humans. In this episode, Dr Andree Bates interviews Dr Morteza Zihayat of Heisenberg Network to explore Human-Centred AI: designing intelligent systems that prioritise welfare, autonomy, dignity and trust over pure technical capability.

Morteza explains how his background in data mining led to a key insight: numbers can be incredibly powerful, but they still “miss the mark” when human values are ignored. That perspective shaped his work in Human-Centred AI and helped inspire Heisenberg Network, a decentralised infrastructure designed to transform fragmented, messy data into high-quality, AI-ready intelligence — in minutes rather than weeks — while dramatically reducing cost.

The conversation digs into what human-centred design looks like in practice. Morteza introduces a “ladder of priorities” to manage conflicting values — from safety and laws at the top, through ethics and platform policies, down to individual preferences — and argues that making this ladder public gives people a right to debate and appeal decisions when something feels wrong.

Finally, Morteza tackles the unavoidable trade-offs between performance and fairness, sharing a real example from mental health research where reducing model accuracy produced a fairer system that clinicians felt more comfortable using. His key message is urgent: powerful AI is coming either way — making it human-centred is a choice we need to commit to now, with transparency becoming non-negotiable.

Topics Covered

• What Human-Centred AI means in practice

• Turning user stories into concrete design checks

• The “ladder of priorities” for managing conflicting values

• Designing systems for welfare, autonomy, dignity and trust

• Power caps and keeping AI controllable

• Personalisation vs wellbeing (and avoiding filter bubbles)

• Where bias enters: data, models, and feedback loops

• Debiasing pipelines and fairness as real-world data shifts

• Learning from users without storing raw personal data

• Explainability challenges with reasoning models

• Why transparency is becoming non-negotiable

• When less accuracy can be the more responsible choice

About the Podcast

AI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.

This show brings together leading experts and changemakers to demystify AI and show how it’s being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI’s role in social good, this podcast offers valuable insights.

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget.

As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses.

This podcast features many experts who have developed powerful AI powered tools that are the secret behind some time saving and supercharged revenue generating business.

Dr. Andree Bates LinkedIn | Facebook | X

Designing proteins that have never existed in nature is no longer sci-fi — it’s becoming a real drug discovery strategy. In this episode, Kashif Sadiq, Founder & CEO of DenovAI Biotech, explains how AI is powering a shift from searching for biologic binders to intentionally designing new proteins from scratch.

Kashif shares his journey from studying physics at University of Cambridge into computational biophysics, and how breakthroughs like AlphaFold from DeepMind helped unlock the next frontier: de novo protein design. Instead of hoping evolution has already produced a usable molecule, Kashif describes how modern AI can engineer bespoke proteins for specific functions, including challenging targets where traditional approaches come up short.

The conversation dives into the sheer scale of “protein space” and why evolution has only explored a tiny fraction of what’s possible. Kashif outlines how this opens the door to targeting diseases and biological mechanisms that have historically been considered undruggable, especially where flat protein interfaces or complex signalling pathways have made small molecules ineffective.

Finally, Kashif explains why combining generative AI with physics-based methods is essential to reduce false positives, improve real-world binding performance, and enable “one-shot design” — where discovery and optimisation become a single integrated process. He also shares what keeps him up at night: clinical trial attrition — and why designing better earlier may be the key to improving success later.

Topics Covered

• De novo protein design vs traditional biologics discovery

• Why evolution explored only a tiny fraction of protein space

• “Programmable biologics” and intentional molecular design

• Alpha Design and designing proteins from the inverse problem

• Antibodies, nanobodies, and therapeutic protein engineering

• Combining generative AI with physics-based validation

• Reducing false positives in protein binding predictions

• “One-shot design” and compressing discovery timelines

• Undruggable targets, flat interfaces, and intracellular signalling

• Clinical trial attrition and what’s missing at the preclinical stage

• When the first de novo-designed therapeutic could enter trials
  

About the Podcast

AI for Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical, no-fluff conversations that demystify AI for biopharma execs — from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, market access, medical affairs, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

Large Language Models (LLMs) are moving far beyond text generation—and into the heart of scientific discovery and pharmaceutical research. In this episode, Javier Tordable, founder and CEO of Pauling.ai and former Google technologist, explains how agentic AI systems are transforming early-stage drug discovery.

Javier shares how modern LLMs differ from earlier generations, highlighting their ability to perform autonomous, multi-step scientific workflows rather than isolated tasks. These AI agents can read and synthesize massive volumes of scientific literature, generate novel hypotheses, validate ideas against published research, and accelerate computational chemistry simulations that once took months or years.

The discussion dives into how LLMs are being used today to identify drug targets, design molecules, optimize clinical trials, and reduce manual scientific labor, while still preserving the critical role of human creativity and experimental validation. Javier also addresses the real risks—hallucinations, data quality, reproducibility—and why hybrid AI + physics-based approaches are essential for trustworthy results.

This episode is a must-listen for researchers, founders, and operators exploring LLMs in biotech, pharma AI, scientific automation, and computational drug discovery.

Topics Covered

1. Large Language Models (LLMs) in science and pharma
2. AI agents and autonomous research workflows
3. Early-stage drug discovery acceleration
4. Computational chemistry and molecular simulations
5. AI-assisted literature review and hypothesis generation
6. Clinical trial optimization with AI
7. Reducing R&D timelines and operational bottlenecks
8. Risks, hallucinations, and validation in scientific AI

About the Podcast

AI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.

This show brings together leading experts and changemakers to demystify AI and show how it’s being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI’s role in social good, this podcast offers valuable insights.

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr. Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget.

As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses.

This podcast features many experts who have developed powerful AI powered tools that are the secret behind some time saving and supercharged revenue generating business results. Those who share their stories and expertise show how AI can be applied to sales, marketing, production, social media, psychology, customer insights and so much more.

Dr. Andree Bates LinkedIn | Facebook | Twitter

Artificial intelligence is rapidly reshaping the pharmaceutical industry—and nowhere is that more evident than in small-molecule drug discovery. In this episode, we sit down with Tom Shani, CEO and co-founder of ProPhet, an AI-driven biotech company focused on discovering drugs for hard-to-target proteins.

Tom explains how machine learning models, transformers, and AI-driven molecular representations are overcoming the biggest limitations of traditional drug discovery: slow timelines, high failure rates, missing data, and billion-dollar R&D costs. Rather than relying solely on physics-based simulations and trial-and-error lab work, AI systems learn patterns directly from noisy biological data—making them uniquely suited for real-world biology.

The conversation explores how AI can compress drug discovery timelines from decades to years, reduce failed trials, and dramatically lower costs by improving early-stage target and molecule selection. Tom also breaks down why small molecules remain the backbone of modern medicine, how AI enables scalable exploration of vast chemical space, and why trust, regulation, and validation remain the biggest hurdles to adoption.

This episode is essential listening for anyone working in pharma R&D, biotech, AI-driven drug discovery, computational biology, or life sciences innovation.

Topics Covered

1. AI-powered small-molecule drug discovery
2. Machine learning vs traditional pharmaceutical R&D
3. Hard-to-drug proteins and undruggable targets
4. Transformers, AlphaFold, and molecular representations
5. Reducing drug discovery timelines and costs
6. AI robustness to missing and noisy biological data
7. Off-target effects, toxicity, and safety prediction
8. The future of AI in pharma and biotech startups

About the Podcast

AI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.

This show brings together leading experts and changemakers to demystify AI and show how it’s being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI’s role in social good, this podcast offers valuable insights.

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr. Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget.

As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses.

This podcast features many experts who have developed powerful AI powered tools that are the secret behind some time saving and supercharged revenue generating business results. Those who share their stories and expertise show how AI can be applied to sales, marketing, production, social media, psychology, customer insights and so much more.

Dr. Andree Bates LinkedIn | Facebook | Twitter