The 34th annual edition of an EU contest for teenage researchers wrapped up this past week with participants from Canada, Denmark, Poland and Portugal claiming the top prize. 

By Sofía Manzanaro 

Inês Alves Cerqueira of Portugal just spent five days in Brussels and left with a top EU prize for young scientists. 

But ask 17-year-old Cerqueira what she remembers most about the event, which featured 136 contestants from three dozen countries in Europe and beyond, and the much-coveted award gets hardly any mention. 

No worries 

‘I loved listening to all the projects and having conversations about science without having to worry about people judging me or anything like that,’ she said as the 34th annual EU Contest for Young Scientists (EUCYS) drew to a close in the Belgian capital. 

Worries or not, Cerqueira and the other contestants aged 14 to 20 years were judged by a jury of 22 distinguished scientists and engineers from across Europe as part of the official competition. It featured 85 science projects in the running for first, second and third awards that shared a total of €62 000 in prize money. 

The rewards also include scholarships and visits to institutions such as the European Space Agency, nuclear-research organisation CERN and a forum that brings together eight of the largest research bodies in Europe. 

All the participants had already won first prizes in national science competitions. At EUCYS, four projects won the top prize and received €7 000 each. 

Cerqueira claimed hers with two teammates: Afonso Jorge Soares Nunes and Mário Covas Onofre. The three Portuguese, who come from the northern coastal city of Porto, are exploring the potential of spider silk to treat bone diseases including osteoporosis. 

The EUCYS projects, which ranged from rocket science and chronic-pain drugs to climate demographics and river pollution, were as varied as the backgrounds of the participants, who came from as far away as Canada and South Korea. 

Canadian Elizabeth Chen was another first-prize winner for a project on a cancer therapy. The two other top-award recipients were Maksymilian Gozdur of Poland for an entry on judicial institutions and Martin Stengaard Sørensen of Denmark for an initiative on rocket propulsion systems. 

Bright minds 

‘EUCYS is about rewarding the enthusiasm, passion and curiosity of Europe’s next generation of bright minds finding new solutions to our most pressing challenges,’ said Marc Lemaître, the European Commission’s director-general for research and innovation. 

Eagerness and spirit were on general display at the event. So was camaraderie. 

Noemi Marianna Pia, Pietro Ciceri and Davide Lolla, all 17 year olds from Italy, said they felt themselves winners by having earned spots at EUCYS for a project on sustainable food and described the event as a once-in-a-lifetime chance to mix with fellow young scientists from around the world. 

The three Italians want to develop plant-based alternatives to animal proteins. At their exhibition stand, they talked with contagious excitement about their research while holding dry chickpeas and soybeans. 

Lolla said that, while his pleasures include tucking into a juicy steak, he feels a pressing need to reduce meat consumption to combat climate change and preserve biodiversity. 

Sparkling ideas  

On the other side of the venue, 16-year-old Eleni Makri from Cyprus recalled how a classroom chat about summer plans sparked an idea to use seagrass on many of the island’s beaches to produce fertiliser. 

Her project partner, Themis Themistocleous, eagerly joined the conversation to explain how seagrass can recover phosphate from wastewater. The process involves thermal treatment of the seagrass. 

Themistocleous also expressed pride at having been chosen by Makri as her teammate for the competition. 

‘There were a thousand people, but she chose me!’ he said with a wide grin as Makri playfully shook her head in response. 

Science can also be the outcome of a partnership rather than its trigger. Metka Supej and Brina Poropat of Slovenia were brought together by sports, particularly rowing. 

After years of training on the same team, they decided to research the impact of energy drinks on heart-rate recovery. 

Multiple paths 

As they cheered for one another while preparing to say goodbye, the participants at EUCYS 2023 offered a glimpse of the combination of qualities – personal, intellectual, social and even professional – that turn young people into pioneering researchers. 

Gozdur, the Polish top-prize winner, discovered his passion for judicial matters while working at a law firm. Before that, he wanted to study medicine and even dabbled in the film industry. 

His EUCYS project drew on French and Polish criminal-procedure codes to examine the prospects for “restorative justice” – a central element of which is rehabilitation of the convict. The conclusion reached was that ‘penal populism is not beneficial to any party, especially to the victim’s,’ according to a description. 

Now 19 years old and a law student in Warsaw, Gozdur said he would like international institutions to take up his work so that it influences ‘real-life’ legal norms in the future. 

‘EUCYS showed me that my idea is actually relevant and that it may help societies,’ he said. ‘I would like to fight more for my project.’ 

For Sørensen, the Danish recipient of the top prize, venturing into rocket science as a teenager was no surprise. From the city of Odense, he began computer programming at the age of 10 and was inspired by his father – an electrical engineer – to look into engineering. 

Now 19 years old, Sørensen is striving in his research to create cheaper rocket engines. His project, entitled “Development of small regeneratively cooled rocket propulsion systems”, demonstrated how small rocket engines can be cooled by using a fuel that is a mixture of ethanol and nitrous oxide. 

Sørensen said he’s unsure what his future path will be while expressing interest in pursuing his rocket research.  

‘I would like to continue working on this project,’ he said. ‘And I would like to do something that matters in the world.’ 

Chen, the top-award winner from Canada, has long had a passion for cancer research. 

From childhood, she became involved in fundraisers for a Canadian cancer association and was puzzled about why significant donations had produced no cure. Now 17 years old and in high school, Chen is seeking a therapy that would avoid the often-considerable side effects of conventional treatments. 

Her project focuses on a novel form of immunotherapy based on “CAR-T cells”, which are genetically altered so they can fight cancer more effectively. 

‘I am really interested in going into university right away and then hopefully getting involved in some cancer research because that is just so interesting to me,’ said Chen, who comes from Edmonton. 

The three Portuguese winners – Cerqueira, Nunes and Onofre – said they have developed a partnership as strong as their spider silk and plan to pursue their research while at university with the hope – one day – of conducting clinical studies. 

Called “SPIDER-BACH2”, their project reflects an awareness that osteoporosis will become a growing health challenge worldwide as people live longer. It aims for in vitro production of bone-building cells known as osteoblasts. 

‘The future is bright for us,’ said Nunes. This article was originally published in Horizon, the EU Research and Innovation Magazine.

The enchanting realm of space exploration continues to unfold new wonders with every passing day, sparking a growing interest among individuals to embark on their own cosmic journeys. While exploring space with the aid of private companies that charge fortunes is a privilege usually reserved for billionaire adventurers, there are occasional exceptions that captivate our attention.

Just a few days ago on 8^th September, Virgin Galactic’s third spaceflight set out on a brief mission that seized the spotlight due to some interesting details. Three private explorers, Ken Baxter, Timothy Nash, and Adrian Reynard, two pilots and one instructor, were onboard ‘VSS Unity’. However, the presence of two different and unique passengers added a twist to the journey: fossils of our ancient human ancestors. The fossil remains of two ancient species, two-million-years-old Australopithecus sediba and 250,000 years old Homo naledi,  held in carbon fiber, emblazoned with the South African flag,  were part of the Virgin Galactic’s spacecraft ‘crew’ for a one-hour ride, making them the oldest human species to visit space. Australopithecus sediba’s clavicle (collarbone) and Homo naledi’s thumb bone were chosen for the voyage. Both fossil remains were discovered in the Cradle of Humankind – home to human ancestral remains in South Africa.

The episode undoubtedly prompts questions regarding the underlying reason behind sending these fossil remains into the vast expanse of space in the first place. It profoundly underscores the immense power of symbols, speaking to us in ways words cannot. This voyage was not just a journey through space, but a soulful homage to our ancestors. Their invaluable contributions have sown the seeds of innovation and growth, propelling us to unimaginable heights. Now, as we stretch our hands towards the heavens, we remember them – and in this gesture, we symbolise our eternal gratitude and awe for the path they paved, allowing humanity to quite literally aim for the skies. As Timothy Nash said, ‘It was a moment to contemplate the enterprising spirit of our earliest ancestors, who had embarked on a journey toward exploration and innovation years ago.’

Moreover, the clavicle of the Australopithecus sediba was deliberately chosen given that it was discovered by nine-year-old Mathew Berger, son of Lee Berger, a National Geographic Society explorer, who played a major role in discovering both species and handed over the remains to Timothy Nash for the journey. This story serves as a touching testament to the boundless potential of youth, showing us that even the young can be torchbearers in the realm of science, lighting the path of discovery with their boundless curiosity. The unearthing of Homo naledi in 2013 wasn’t just about finding bones; it was a window into our past. This ancient ancestor, with its apelike shoulders and human-like feet, hands, and brain, wasn’t just a distant relative. They were artists and inventors, leaving behind symbols and tools in their cave homes as a silent testament to their legacy. This led to the discovery of more than 1,500 specimens from one of the biggest excavations in Africa’s history. It wasn’t just about digging up the past; it was about piecing together the jigsaw of our very essence, deepening our understanding of the roots and journey of our kind, especially in the heartland of South Africa. Each discovery, each bone, whispered tales of our shared journey, of beginnings, growth, and the undying spirit of exploration.

For those involved in the venture, the occasion was awe-inspiring as it connected our ancient roots to space exploration. However, not everyone is pleased. The event has sparked criticism from  archaeologists and palaeoanthropologists, many of whom have called it a mere publicity stunt and raised serious concerns over such an act given that it poses risks to the care of the precious fossils. It was further argued that the act was ethically wrong, and lacked  any concrete scientific justifications.

Setting aside this debate, the episode connects chronicles of our past with the boundless potential of humankind’s future. It celebrates the age-old quest for exploration shared across millennia. This journey, captivating in its essence, elevates space exploration to a sacred place where fossils, once cradled by the Earth’s soil, now dance among the stars. Just as with pivotal moments in space history, it is also a compelling cue to states that are currently lagging in this race to timely embrace the possibilities of this frontier. Countries, like Pakistan, should draw inspiration from such milestones to fervently chart their own celestial courses.

Upon their return to South Africa, the relics would be displayed in museums and other institutions, offering a chance to the public to view them and draw inspiration. As we witness the rise of commercial space travel, this unique journey provides glimpses of the multifaceted nature of space exploration – one that prompts us to reflect on our past, engage actively with the present and anticipate the future that awaits us. Something Pakistan’s national poet Allama Iqbal eloquently captured in one his verses, translated as: I see my tomorrow (future) in the mirror of my yesterday (past).

With the advent of Artificial Intelligence technology in the field of chemistry, traditional methods based on experiments and physical models are gradually being supplemented with data-driven machine learning paradigms. Ever more data representations are developed for computer processing, which are constantly being adapted to statistical models that are primarily generative.

Although engineering, finance and business will greatly benefit from the new algorithms, the advantages do not stem only from algorithms. Large-scale computing has been an integral part of physical science tools for decades, and some recent advances in Artificial Intelligence have begun to change the way scientific discoveries are made.

There is great enthusiasm for the outstanding achievements in physical sciences, such as the use of machine learning to reproduce images of black holes or the contribution of AlphaFold, an AI programme developed by DeepMind (Alphabet/Google) to predict the 3D structure of proteins.

One of the main goals of chemistry is to understand matter, its properties and the changes it can undergo. For example, when looking for new superconductors, vaccines or any other material with the properties we desire, we turn to chemistry.

We traditionally think chemistry as being practised in laboratories with test tubes, Erlenmeyer flasks (generally graduated containers with a flat bottom, a conical body and a cylindrical neck) and gas burners. In recent years, however, it has also benefited from developments in the fields of computer science and quantum mechanics, both of which became important in the mid-20th century. Early applications included the use of computers to solve calculations of formulas based on physics, or simulations of chemical systems (albeit far from perfect) by combining theoretical chemistry with computer programming. That work eventually developed into the subgroup now known as computational chemistry. This field began to develop in the 1970s, and Nobel Prizes in chemistry were awarded in 1998 to Britain’s John A. Pople (for his development of computational methods in quantum chemistry: the Pariser-Parr-Pople method), and in 2013 to Austria’s Martin Karplus, South Africa’s Michael Levitt, and Israel’s Arieh Warshel for the development of multiscale models for complex chemical systems.

Indeed, although computational chemistry has gained increasing recognition in recent decades, it is far less important than laboratory experiments, which are the cornerstone of discovery.

Nevertheless, considering the current advances in Artificial Intelligence, data-centred technologies and ever-increasing amounts of data, we may be witnessing a shift whereby computational methods are used not only to assist laboratory experiments, but also to guide and orient them.

Hence how does Artificial Intelligence achieve this transformation? A particular development is the application of machine learning to materials discovery and molecular design, which are two fundamental problems in chemistry.

In traditional methods the design of molecules is roughly divided into several stages. It is important to note that each stage can take several years and many resources, and success is by no means guaranteed. The phases of chemical discovery are the following: synthesis, isolation and testing, validation, approval, commercialisation and marketing.

The discovery phase is based on theoretical frameworks developed over centuries to guide and orient molecular design. However, when looking for “useful” materials (e.g. petroleum gel [Vaseline], polytetrafluoroethylene [Teflon], penicillin, etc.), we must remember that many of them come from compounds commonly found in nature. Moreover, the usefulness of these compounds is often discovered only at a later stage. In contrast, targeted research is a more time-consuming and resource-intensive undertaking (and even in this case it may be necessary to use known “useful” compounds as a starting point). Just to give you an idea, the pharmacologically active chemical space (i.e. the number of molecules) has been estimated at 1060! Even before the testing and sizing phases, manual research in such a space can be time-consuming and resource-intensive. Hence how can Artificial Intelligence get into this and speed up the discovery of the chemical substance?

First of all, machine learning improves the existing methods of simulating chemical environments. We have already mentioned that computational chemistry enables to partially avoid laboratory experiments. Nevertheless, computational chemistry calculations simulating quantum-mechanical processes are poor in terms of both computational cost and accuracy of chemical simulations.

A central problem in computational chemistry is solving the 1926 equation of physicist Erwin Schrödinger’s (1887-1961). The scientist described the behaviour of an electron orbiting the nucleus as that of a standing wave. He therefore proposed an equation, called the wave equation, with which to represent the wave associated with the electron. In this respect, the equation is for complex molecules, i.e. given the positions of a set of nuclei and the total number of electrons, the properties of interest must be calculated. Exact solutions are only possible for single-electron systems, while for other systems we must rely on “good enough” approximations. Furthermore, many common methods for approximating the Schrödinger equation scale exponentially, thus making forced solutions difficult to solve. Over time, many methods have been developed to speed up calculations without sacrificing precision too much. However, even some “cheaper” methods can cause computational bottlenecks.

A way in which Artificial Intelligence can accelerate these calculations is by combining them with machine learning. Another approach fully ignores the modelling of physical processes by directly mapping molecular representations onto desired properties. Both methods enable chemists to more efficiently examine databases for various properties, such as nuclear charge, ionisation energy, etc.

While faster calculations are an improvement, they do not solve the issue that we are still confined to known compounds, which account for only a small part of the active chemical space. We still have to manually specify the molecules we want to analyse. How can we reverse this paradigm and design an algorithm to search the chemical space and find suitable candidate substances? The answer may lie in applying generative models to molecular discovery problems.

But before addressing this topic, it is worth talking about how to represent chemical structures numerically (and what can be used for generative modelling). Many representations have been developed in recent decades, most of which fall into one of the four following categories: strings, text files, matrices and graphs.

Chemical structures can obviously be represented as matrices. Matrix representations of molecules were initially used to facilitate searches in chemical databases. In the early 2000s, however, a new matrix representation called Extended Connectivity Fingerprint (ECFP) was introduced. In computer science, the fingerprint or fingerprint of a file is an alphanumeric sequence or string of bits of a fixed length that identifies that file with the intrinsic characteristics of the file itself. The ECFP was specifically designed to capture features related to molecular activity and is often considered one of the first characterisations in the attempts to predict molecular properties.

Chemical structure information can also be transferred into a text file, a common output of quantum chemistry calculations. These text files can contain very rich information, but are generally not very useful as input for machine learning models. On the other hand, the string representation encodes a lot of information in its syntax. This makes them particularly suitable for generative modelling, just like text generation. Finally, the graph-based representation is more natural. It not only enables us to encode specific properties of the atom in the node embeddings, but also captures chemical bonds in the edge embeddings. Furthermore, when combined with message exchange, graph-based representation enables us to interpret (and configure) the influence of one node on another node by its neighbours, which reflects the way atoms in a chemical structure interact with each other. These properties make graph-based representations the preferred type of input representation for deep learning models. (1. continued)