Last week, I found myself surrounded by nearly 100 early-career researchers at a doctoral conference we organized with the topic "beyond the defense" in mind. The energy was infectious—brilliant minds presenting their research, from novel cancer therapeutics to diagnostic tools. But as the day progressed, I noticed a pattern in the conversations during coffee breaks. The most common question wasn't about methodologies or results—it was "What comes next?"

As PhD students, we spend years mastering the art of discovery, meticulously designing experiments and pushing the boundaries of human knowledge. But rarely do we learn what happens when our discoveries need to become real-world solutions that actually help patients. That gap between the bench and bedside? It's where most academic breakthroughs go to die.

This question hit particularly close to home because I've been asking it myself. As I navigate my own PhD journey in drug discovery through the Marie Curie ALLODD network, I've become increasingly curious about the innovation ecosystem—the bridge between academic labs and patient impact. So when an opportunity arose to explore medical technology valuation through a unique collaboration between Karolinska Institute and the University of Minnesota's Carlson School of Management, I jumped at it. What I discovered completely changed how I think about innovation.

When Scientists Meet MBAs: A Meeting of Minds
The course format was brilliantly simple: put PhD students from KI together with MBA students from Carlson, give them real medical technology assessment challenges, and watch what happens when two completely different worldviews collide.
On one side, you had us—the scientists. We spoke in terms of mechanism of action, clinical endpoints, and regulatory pathways. We could dissect a drug's molecular target with surgical precision but would get lost when asked about market penetration strategies. On the other side were the MBA students, fluent in financial modeling, competitive analysis, and go-to-market strategies, but who might struggle to distinguish between a small molecule and a biologic.
The magic happened in the collision.

Working through technology valuation cases, I watched as my MBA teammates approached our drug discovery research with questions I'd never considered: "What's the total addressable market? Who are the key competitors? What's your intellectual property position? How does reimbursement work in different healthcare systems?" Meanwhile, they were fascinated by our ability to assess technical risk, understand regulatory science, and evaluate whether a proposed mechanism was actually feasible.

One particularly eye-opening moment came when we were evaluating a novel diagnostic technology. I immediately dove into the technical specifications—sensitivity, specificity, and analytical validation requirements. My MBA partner looked at the same technology and asked, "But who's going to pay for this? How does it fit into existing clinical workflows? What's the cost per test?" Both perspectives were essential; neither alone would have led to an accurate assessment.

The course taught us frameworks for technology valuation that combined both lenses: discounted cash flow analysis that accounted for technical risk, real options valuation that considered both scientific and commercial uncertainties, and market assessment that factored in regulatory timelines. But more importantly, it showed us how innovation actually happens—not in isolation, but through collaboration between complementary skill sets.

The Hidden Reality of Innovation
This experience illuminated something crucial about the technology transfer ecosystem that isn't taught in graduate school: successful innovation requires translation, not just discovery.

Most academic discoveries never make it to patients not because the science is bad, but because there's a fundamental communication gap between the worlds of research and business. Scientists are trained to think about statistical significance and mechanistic understanding. Investors think about market size and return on investment. Regulators think about safety and efficacy. Clinicians think about workflow integration and patient outcomes.

These aren't competing priorities—they're all essential pieces of the same puzzle. But too often, they exist in silos.

Universities have technology transfer offices designed to bridge this gap, but the reality is more complex. A typical tech transfer process involves invention disclosure, patent application, market assessment, licensing negotiations, and ongoing relationship management. Each step requires different expertise and different ways of thinking about the same underlying science.

The most successful examples of academic technology transfer happen when teams understand multiple perspectives from the start. Think about the development of CAR-T cell therapy—it required not just immunology expertise, but also manufacturing know-how, regulatory strategy, and business model innovation. The scientists who founded companies like Kite Pharma didn't just make scientific breakthroughs; they learned to speak multiple languages.

The Skills They Don't Teach in Graduate School
Reflecting on the KI-Carlson experience, I realized how many crucial skills are missing from traditional PhD training:

Market Awareness: Understanding not just whether your research could work, but whether anyone would want it and pay for it. This means learning to assess competitive landscapes, understand healthcare economics, and think about adoption barriers.

Financial Literacy: Being able to build basic financial models, understand investment criteria, and communicate value propositions in business terms. You don't need an MBA, but you need to understand how investors think.

Regulatory Intelligence: Knowing how your research fits into approval pathways, what evidence standards apply, and how regulatory requirements shape development strategies. This is especially crucial in drug discovery, where regulatory risk can make or break a program.

Cross-Functional Communication: The ability to translate complex scientific concepts for non-scientific audiences without dumbing them down. This isn't just about making pretty slides—it's about understanding what different stakeholders care about and framing your work accordingly.

Partnership Building: Most innovations succeed through collaboration, not heroic individual efforts. Learning to identify complementary expertise and build productive working relationships across disciplines is essential.

Your Research, Your Future: Practical Next Steps
So what does this mean for you as a PhD student? Start by honestly evaluating your research's innovation potential:

• Assess the landscape: Who else is working on similar problems? What approaches have failed and why? Where are the white spaces?
• Understand your value proposition: What makes your approach unique? What problems does it solve that current solutions don't address?
• Map the pathway: What would it take to get from your current research to a real-world application? What are the key technical, regulatory, and commercial hurdles?
• Build your network: Connect with your university's technology transfer office. Attend industry conferences. Find mentors who've made the transition from academia to industry or entrepreneurship.

Consider seeking out experiences like the KI-Carlson program. Many universities offer innovation and entrepreneurship courses designed for scientists. Organizations like AAAS, NIH, and various industry associations provide workshops on technology transfer and commercialization.

Most importantly, start thinking about innovation early in your PhD, not as an afterthought. The decisions you make about research direction, intellectual property, and collaboration can significantly impact the ultimate real-world potential of your work.

Bridging Two Worlds
That room of 100 eager PhD students represents incredible untapped potential—not just for scientific discovery, but for innovation that changes lives. Each person there is working on research that could potentially help patients, improve healthcare, or solve pressing global challenges. But potential alone isn't enough.

The KI-Carlson collaboration taught me that innovation isn't just about having great ideas; it's about building bridges between different worlds of expertise. It's about learning to see your research through multiple lenses and finding collaborators who complement your strengths.

Understanding the path from bench to bedside isn't just about career options—though it certainly opens doors to industry, consulting, venture capital, and entrepreneurship. It's about maximizing the impact of all those late nights in the lab, all those failed experiments that taught you something new, and all that passion for discovery that got you into science in the first place.
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Because at the end of the day, the goal isn't just to publish papers or graduate with a PhD. It's to contribute to human knowledge in ways that make the world a little bit better. And sometimes, that requires learning to speak more than one language.

Attending a scientific conference in Athens as an Early Stage Researcher was a fantastic mix of learning, networking, and enjoying the city. The event brought together experts and young scientists working on allostery—a key concept in understanding how proteins change shape and regulate biological processes, with big implications for drug discovery.

The first day was all about progress. All 14 ESRs presented updates on their projects, and it was rewarding to see three years of work coming together with strong results.

On the second day, the focus shifted from science to careers. We had a workshop on applying for jobs in the industry, covering everything from CV tips to what hiring managers really look for. A roundtable discussion with professionals who’ve worked in both academia and industry was particularly insightful—hearing their perspectives made me think more carefully about my own career path.

The next three days were packed with scientific talks, split into sessions on understanding allostery, finding allosteric drug targets, and studying how proteins respond to these subtle molecular changes. For many of us, this was our first time presenting at a major conference, and speaking in front of experts was both nerve-wracking and exciting. It pushed me out of my comfort zone, but the discussions afterwards made it worth it. The poster session that followed the third day’s talks gave me the chance to discuss my research with other scientists in the field, exchanging ideas and getting useful feedback.

Of course, it wasn’t all work. Evenings were spent exploring Athens—good food, great conversations, and a bit of sightseeing. From lively tavernas to strolls through the city’s historic streets, those moments were just as valuable as the science.
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Looking back, the conference was more than just presentations and networking—it was a chance to grow, make connections, and even have some fun along the way. And if future scientific events are anything like this one, I’ll be happy to attend more.

Over a century ago, Christian Bohr observed that carbon dioxide affects oxygen binding to hemoglobin—a phenomenon called the Bohr effect, an early glimpse into allostery. Fast forward to 1961, Jacques Monod and François Jacob coined the term “allosteric inhibition,” setting the stage for decades of groundbreaking research.

At its core, allostery describes how a molecule binding at one site of a protein can influence another distant site, namely the active site, enabling regulation at a distance—a fundamental feature of life. This is due to the fact that binding sites are energetically connected/coupled. But how does it work? Scientists developed three key models to explain it:

1. MWC Model (Concerted Model) – The protein switches between two states as a whole. Ligand binding shifts equilibrium toward the active state, like a team moving together.
2. KNF Model (Sequential Model) – Instead of an all-at-once switch, changes happen step by step, propagating structural shifts gradually across the protein, like a domino effect.
3. Ensemble Allostery Model (EAM) – Ensemble Allostery Model (EAM) – A modern and comprehensive model, integrating both MWC and KNF:
   ✅ Proteins exist in multiple conformations at once.
   ✅ Ligand binding reshapes the energy landscape, shifting populations dynamically.
   ✅ Distant sites are affected in a flexible, adaptable manner.

EAM is currently the most realistic, dynamic, and widely accepted view of allostery, revolutionizing our understanding of molecular regulation and drug discovery.

From Experiment to Computation: Cracking the Allosteric Code
For decades, X-ray crystallography provided key snapshots of allosteric proteins. But since allostery is a dynamic process, techniques like NMR spectroscopy, hydrogen-deuterium exchange mass spectrometry (HDXMS), all-atom molecular dynamics simulation and coarse-grained simulations are able to reveal hidden transient states and allosteric networks.
With advances in computational biology, molecular simulations allow us to even predict allosteric sites, simulate population shifts, and design allosteric drugs. The first FDA-approved allosteric drug in 2004 was a milestone—today, allosteric modulation is revolutionizing drug discovery.

At ALLODD, we are actively investigating allostery to push the boundaries of drug discovery. As part of this initiative, 14 PhD students are being trained in allosteric drug discovery, working to unravel allosteric mechanisms and develop novel therapeutic strategies.

The Future of Allostery: Unlocking the "Second Secret of Life"
Monod called allostery "the second secret of life", and its full potential is only just being uncovered. By linking genetic codes with allosteric mechanisms, we may soon crack the allosteric code, offering new ways to tackle diseases like cancer.

Despite decades of research, many questions remain:
🔹 How do entropy and enthalpy drive allostery?
🔹 What role does it play in protein disorder?
🔹 Can allosteric networks be fully mapped in living cells?
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One thing is clear: Allostery is no longer just a biochemical curiosity— it’s rooted in the fundamental physical properties of macromolecular systems and it’s the key to unlocking new biology and next-generation therapies

​Image credits: Raza, S.H.A., Zhong, R., Yu, X. et al. Advances of Predicting Allosteric Mechanisms Through Protein Contact in New Technologies and Their Application. Mol Biotechnol 66, 3385–3397 (2024). https://doi.org/10.1007/s12033-023-00951-4

I recently submitted my thesis, and while completing a PhD thesis is a significant milestone, the submission process can be quite overwhelming due to various administrative requirements. To help fellow PhD students navigate this often-overlooked aspect of their journey, here are some practical tips:

1. Understand Requirements: Familiarize yourself with your university's guidelines for thesis formatting and submission. Each institution has specific rules you need to follow, including downloadable templates.

2. Plan Your Story: Embed your research in a coherent narrative. Draft a plan and identify any gaps, especially in the results section, to determine which remaining experiments need to be completed.

3. Start Early with Administrative Tasks: Begin collecting the necessary documents and signatures well in advance of your submission deadline. Keep in mind that people may be sick, on holiday, or busy with other commitments. In Germany, I had to submit additional documents such as certified copies of my diplomas and letters to the dean and the university —none of which could be digitalized. This required extra printing time!

4. Organize Your Documents: Save all thesis-related materials on a drive, including drafts, feedback, and administrative forms, to avoid last-minute scrambling.

5. Seek Support: Don’t hesitate to reach out to your committee, peers, or administrative staff if you have questions or need clarification about the submission process. They are familiar with the requirements and can provide valuable insights.
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Good luck to all PhD candidates! 

​My ~150 pages of cumulative thesis along with 50+ printed documents required for the submission

As Stockholm prepares to host the Nobel week this coming December, the rest of the world is still discussing the recently awarded Nobel prizes in chemistry and physics. This year's awards have sparked intense debate, as they highlight the growing influence of artificial intelligence (AI) in scientific research. The recognition of AI-driven discoveries in both chemistry and physics has raised questions about the future of scientific inquiry and the role of technology in advancing our understanding of the natural world.

The 2024 Nobel Prize in Chemistry was awarded jointly to David Baker, Demis Hassabis, and John Jumper for their groundbreaking work in protein structure prediction and design. Baker was recognized for his achievements in computational protein design, while Hassabis and Jumper were honored for their development of AlphaFold2, an AI model that can accurately predict protein structures. The influence of AlphaFold reaches far beyond academia; it has made protein structure predictions widely accessible through the AlphaFold Protein Structure Database, which now boasts over 214 million predicted structures. As ALLODD is a drug discovery-focused consortium, these AlphaFold predictions are particularly valuable for our research, potentially accelerating the identification of novel drug targets and the design of more effective therapeutics.

In an unexpected turn, the Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey Hinton for their seminal contributions to the foundational methods that enabled the development of machine learning. Hinton, often referred to as the "Godfather of AI," was recognized for his pioneering work in artificial neural networks. This decision has sparked discussions about the boundaries between traditional scientific disciplines and the emerging field of AI.
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The recognition of AI-driven research in both chemistry and physics has elicited mixed reactions from the scientific community. Some researchers have questioned whether these awards align with the traditional scope of these disciplines. However, others argue that these awards reflect the transformative impact of AI on scientific research. As we reflect on these awards, it's clear that AI is reshaping the landscape of scientific discovery. While some may debate the timing of these recognitions, there's no denying the profound impact that AI-driven research is having across multiple disciplines.

P.S. If you ever are in Stockholm in the first week of December, make sure to make the most of the Nobel week events!
- Go visit the lectures by the newly awarded laureates at Karolinska, Stockholm University, and KTH.
- Enjoy the light illuminations dedicated to past Nobel Prize awards scattered throughout the city! Check out the program here: Nobel Week Lights – Stockholm | 2024; the lights on Stadshuset are usually the most impressive.
- Don't skip the Nobel Prize Museum; even if you're a science geek, it can surprise you with some secret facts about the laureates.

 Image credit: Nobel Week Lights 2024 - Visit Stockholm

​Last month, I had the amazing opportunity to return not only to Barcelona but more specifically my hometown neighbourhood, where I attended the EuroQSAR 2024 conference.

The event covered a wide range of topics within the Drug Discovery field, including advancements in AI and machine learning for QSAR modelling, 3D-QSAR techniques, molecular dynamics simulations, and cheminformatics integration. The conference also explored emerging technologies like multi-omics integration, ligand-based virtual screening, and the ethical implications of AI in drug discovery, alongside workshops and discussions on practical applications, data transparency, and interdisciplinary collaboration.

During that week, I had the opportunity to present my research to my peers in a poster session, where I received very interesting feedback from colleagues and members of my former research group who were also attending the event.
Of course, no conference would be complete without a gala dinner, and we were taken all the way up to Tibidabo mountain, to the Observatori Fabra.

We enjoyed a lovely dinner with the best possible view of Barcelona, followed by a guided visit to the observatory. To culminate the night, we had the chance to observe Saturn through the 120-year-old telescope and take in a 360-degree view of the city as we walked around the dome.
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I felt extremely lucky to be back home and enjoy my city while also enjoying the best science.

Hi everyone! It's time for my ALLODD blogpost :)

This past July and August, I had the exciting opportunity to work at Forschungszentrum Jülich (FZJ) as part of my secondment with ALLODD.

During my time there, I focused on QM/MM simulations of the glycine receptor to better characterise cation-pi interactions, a key element in understanding receptor behaviour and function.

Secondments are invaluable training tools, especially for PhD students. In this blog post, I will highlight their importance.

In many PhD programs, and especially ITNs, ESRs have the chance to get trained through secondments which according to the definition are a „detachment of a person from their regular organization for a temporary assignment elsewhere”.  Secondments essentially represent a period spent abroad working in a different workplace (labs in our cases), focusing on a specific project. We could thus say that metaphorically speaking, they represent the equivalent of a mission for us for scientific growth improvement. But what do exactly secondments mean to us? And why can they be so important?

Like other forms of detachments, surely a contributing role is played by the push they provide in getting out of our comfort zones and routines, adapting to new environments and scientific approaches, and settings. However, they primarily represent a tool for broader expertise development and training. 

 These periods abroad allow us to work in different labs, focusing on specific projects that may be related to or detach from our primary research. The goal is to develop aspects of the project that we are either unable to explore in our usual settings, can only explore partially, or that may require the integration of different approaches. These experiences are thus invaluable for gaining new perspectives on research problems.

Additionally, these exchanges allow us to take advantage of local equipment and environments, providing flexibility and experience with similar software and tools, or even the opportunity to use new ones, which is also important.  Upon returning, we can merge and enhance the knowledge gained with our primary lab's expertise. In addition, they often allow us to dive into different research fields and learn or get a glimpse at the deeper and complex variegations of research allowing us to get a deeper understanding of the challenges to face. They may also allow us to address what we are facing in our field with a more informed perspective.

Personally, in fact, I have found these tools very insightful and useful. As a synthetic chemist, I often notice the tendency to think of molecules and chemical modifications in a somewhat more simplistic or "plain" manner, while the computational approaches explored in some of my secondments have allowed me to recognize the deeper complexity of the drug optimization and to understand that the process should never be reduced to a simple, two-dimensional view.

To sum up, my secondments have allowed me hands-on experience with computational software used in drug discovery, exposed me to different mindsets and approaches and even joined engaging scientific discussions which would have been less likely to happen! Secondments offer not only the chance to learn new techniques but also the opportunity to engage with diverse scientific mindsets and approaches. This opens up the possibility of meaningful collaborations that might not be possible or would happen differently if I had stayed in my home country.

These are just some of the many benefits: meeting other experts, hearing personal stories, and seeing how different paths and minds intersect, I do believe this fosters networking, and contributes to a shared European identity. The different approaches to the projects, meetings, interpretation of data and scientific papers, and software, even when using the same software or working on similar tasks, are intriguing and open-minding.

Therefore, I would define these scientific tools as essential allowing a better exchange, as they facilitate exchange. After all, isn’t scientific progress built on the exchange of ideas and discoveries (and thus minds)?

So that’s how I would spell/summarise them out as: 
Scientific
Enriching
Correspondences
On
Novel
Data-exploration
Matching
Engaging
Networking
Team-building
Strategies
 
Thank you, ALLODD and the European Commission, for supporting this!
Regards,
ESR7 Vincenzo Di Lorenzo

Summertime is usually the time of the year when things slow down, and the lab gets a bit quieter. A good time to catch up on reading all those scientific papers that accumulate throughout the year in my “to read” bookmark folder or as a pile of printouts on my desk. With the summer (at least in Berlin) coming to a close, this is a good occasion to reflect on practices of how to keep up with scientific literature – a topic haunting me at times given the almost infinite and ever-increasing number of interesting papers out there.

For me personally, scientific papers fall into two categories. Category 1 includes scientific papers that are directly related to my field. These are publications dealing with “my” target protein, or reporting synthesis procedures that I want to apply. The second category more broadly entails reviews, perspectives and case studies unrelated to my daily work. While I usually make time to read “category 1 papers” as soon as possible the latter ones accumulate in my web browser and on my desk.

Let’s first reflect on how to find relevant scientific papers. Probably the most obvious are field-specific scientific journals. Through a subscription to their article alerts, you’ll receive regular updates on new research published. A second strategy that I find useful is to use citation tracking for seminal papers, e.g. in Google Scholar. It allows you to discover the latest research building on these groundbreaking articles and thereby stay up to date on a research topic. Finally, I often find inspiration for readings in online discussion groups and blog posts. These channels have the advantage that they provide, apart from the references themselves, other researcher’s opinions, criticisms and ideas on the respective publications in the form of comments. Translated into the real world, journal clubs are a great opportunity to uncover new literature as well, and discuss it with peers of course.

Once the sources for relevant literature are established, we should turn to a more critical aspect: How to make time to browse and read scientific literature. One strategy to maintain a steady reading pace is to set aside dedicated time for reading. While this may work for some people, I never managed to uphold such a routine. Rather I try to use regular “downtime” for reading, e.g. during waiting time in the lab or commutes.

Finally, let’s review strategies for efficient reading and note-taking. During one of our early ALLODD workshops, I learned the following approach to scientific reading that I have since adopted: Starting from the abstract, I continue by reading the conclusions section of a paper. This helps me to decide whether the paper is worth a deeper read. If it is, I either focus on the figures and tables to identify paragraphs that hold the information that I am looking for, or I continue reading the entire piece from introduction to end. While reading, I like taking notes. I use colour-coding to highlight text and categorize my notes into “hypotheses”, “key findings”, “open questions”, etc. I also find it useful to summarize key points right next to a paper’s title. You may also want to highlight critical data, e.g. the potency of a reference compound that you may want to refer back to later. Likewise, some citation managers allow the addition of keywords or notes to imported publications, which can help retrieve studies at a later point in time. When your goal is comprehensive literature research on a given topic, you may want to compile your notes alongside screenshots and schemes into a larger document that summarizes the findings. 

In summary, keeping up with the literature next to daily lab work and writing a PhD thesis is a continuous and at times challenging task. Setting up article alerts, using citation tracking as well as leveraging social media helps to discover relevant publications. Whether you decide on a dedicated time for reading or squeeze it in during downtime in the lab, efficient reading and annotation strategies alongside citation programs help to manage the sometimes daunting amount of information.
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Do you have any strategies to find, read and annotate scientific literature? Feel free to share them in the comments section!

In October 2023, I had the wonderful opportunity to complete a two-month secondment at the CAMD (Computer Assisted Drug Design) lab in Urbino, Italy, under the supervision of Prof Giovanni Bottegoni. As a purely experimental biochemist, this was my first foray into an actual computational lab and I found the experience very enlightening. Of course, I had had previous introductions to computational methods and case studies from some of my Biochemistry courses, as well as from presentations at various conferences, and not the least from my fellow ESRs hailing from the computational side of the allostery field. However, in this case, I found the adage “experience is the best teacher” to be true. My hands-on use of tools such as Schrödinger and VMD showed me in a concrete way what it must be like to be a computational scientist, and I now have a better understanding of the typical workflow, as well as of the stakes of such work, including the balance between making faster versus more detailed simulations. Additionally, although my own training focused on proteins I was already familiar with from my experimental projects (the melanocortin receptors), several of my colleagues in Urbino were starting various new projects on completely different targets. This contrasts greatly with how experimental labs usually function with the focus remaining largely on related and/or functionally similar proteins across most projects. Despite this, at the CAMD lab, we could still help each other out, which fostered a real sense of camaraderie and a greater understanding of each other’s goals. I believe, now more than ever, that the future of science, and drug discovery in particular, truly relies on, not just one or the other, but the symbiosis of both computational and experimental methods. Thus, it is crucial to understand the possibilities as well as the limits of both, in order to lead to optimal collaborations.

Finally, I would like to highlight the backdrop of my journey into the computational world: the gorgeous city of Urbino, relatively unknown internationally, but a marvel of Renaissance arts and architecture, as anyone attending the recent ALLODD conference there surely noticed. I was happy to return once more and have the chance to say a quick hello to my former colleagues at the CAMD lab.