Demo Data from EDBT 2026 / reviewers in the wild / expert
Teodoro Laino
dblp:77/1853
· DBLP profile ↗
7ranked-venue papers
0as first author
4since 2021 · last 2025
0000-0001-8717-0456ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 since 2021Systems, architecture and hardware · 3 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
4 papers |
Bioinformatics and computational biology · 92% Computational science and engineering · 8% | |
| Artificial intelligence
3 papers |
Generative modeling · 50% Learning paradigms · 38% Vision and language · 13% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 100% |
Topics — the 13 heaviest of 14, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
molecular property prediction |
0.8 | 1 | 2024 | Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry · NeurIPS 2024 |
Bioinformatics and computational biology › structural biology
molecular structure determination |
0.8 | 1 | 2024 | Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry · NeurIPS 2024 |
Machine learning › Learning paradigms › multi-task learning
multi-task language model |
0.7 | 1 | 2023 | Unifying Molecular and Textual Representations via Multi-task Language Modelling · ICML 2023 |
Machine learning › Generative modeling
molecular generation |
0.4 | 1 | 2020 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models · NeurIPS 2020 |
Machine learning › Generative modeling
variational autoencoder |
0.4 | 1 | 2020 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models · NeurIPS 2020 |
Bioinformatics and computational biology › drug discovery › drug design
de novo drug design |
0.4 | 1 | 2020 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models · NeurIPS 2020 |
Bioinformatics and computational biology
drug discovery |
0.4 | 1 | 2020 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models · NeurIPS 2020 |
Computational science and engineering
computational chemistry |
0.2 | 1 | 2016 | Enhanced MPSM3 for applications to quantum biological simulations · SC 2016 |
High-performance computing › sparse linear algebra
sparse matrix computation |
0.2 | 1 | 2016 | Enhanced MPSM3 for applications to quantum biological simulations · SC 2016 |
High-performance computing › sparse linear algebra
sparse matrix multiplication |
0.2 | 1 | 2016 | Enhanced MPSM3 for applications to quantum biological simulations · SC 2016 |
Computer vision › Vision and language › vision-language model
multimodal large language model |
0.2 | 1 | 2024 | Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry · NeurIPS 2024 |
Bioinformatics and computational biology › molecular informatics
cheminformatics |
0.2 | 1 | 2023 | Unifying Molecular and Textual Representations via Multi-task Language Modelling · ICML 2023 |
Bioinformatics and computational biology › molecular informatics › cheminformatics
molecular representation |
0.2 | 1 | 2023 | Unifying Molecular and Textual Representations via Multi-task Language Modelling · ICML 2023 |
Methods — techniques the papers use, named apart from their topics
spectroscopic simulation · 1.5multimodal learning · 1.5multi-task learning · 1.3language modelling · 1.3toxicity prediction · 0.9retrosynthesis prediction · 0.9docking simulation · 0.9controlled sampling · 0.9binding affinity prediction · 0.9density matrix multiplication · 0.5self-consistent field method · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Integrating genetic algorithms and language models for enhanced enzyme designabstractEnzymes are molecular machines optimized by nature to allow otherwise impossible chemical processes to occur. Their design is a challenging task due to the complexity of the protein space and the intricate relationships between sequence, structure, and function. Recently, large language models (LLMs) have emerged as powerful tools for modeling and analyzing biological sequences, but their application to protein design is limited by the high cardinality of the protein space. This study introduces a framework that combines LLMs with genetic algorithms (GAs) to optimize enzymes. LLMs are trained on a large dataset of protein sequences to learn relationships between amino acid residues linked to structure and function. This knowledge is then leveraged by GAs to efficiently search for sequences with improved catalytic performance. We focused on two optimization tasks: improving the feasibility of biochemical reactions and increasing their turnover rate. Systematic evaluations on 105 biocatalytic reactions demonstrated that the LLM-GA framework generated mutants outperforming the wild-type enzymes in terms of feasibility in 90% of the instances. Further in-depth evaluation of seven reactions reveals the power of this methodology to make "the best of both worlds" and create mutants with structural features and flexibility comparable with the wild types. Our approach advances the state-of-the-art computational design of biocatalysts, ultimately opening opportunities for more sustainable chemical processes. Yves Gaetan Nana Teukam, Federico Zipoli, Teodoro Laino, Emanuele Criscuolo, Francesca Grisoni, Matteo Manica |
Briefings Bioinform. | 3 |
| 2024 | Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for ChemistryabstractSpectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight into the molecular structure, including the presence or absence of functional groups. Chemists leverage the complementary nature of the different methods to their advantage. However, the lack of a comprehensive multimodal dataset, containing spectra from a variety of spectroscopic techniques, has limited machine-learning approaches mostly to single-modality tasks for predicting molecular structures from spectra. Here we introduce a dataset comprising simulated $^1$H-NMR, $^{13}$C-NMR, HSQC-NMR, Infrared, and Mass spectra (positive and negative ion modes) for 790k molecules extracted from chemical reactions in patent data. This dataset enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach employed by human experts. Additionally, we provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions. This dataset has the potential automate structure elucidation, streamlining the molecular discovery pipeline from synthesis to structure determination. The dataset and code for the benchmarks can be found at https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset (Available upon submission of the supporting information). Marvin Alberts, Oliver Schilter, Federico Zipoli, Nina Hartrampf, Teodoro Laino |
NeurIPS | 5 |
| 2024 | Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev |
Future Gener. Comput. Syst. | 58 |
| 2023 | Unifying Molecular and Textual Representations via Multi-task Language ModellingabstractThe recent advances in neural language models have also been successfully applied to the field of chemistry, offering generative solutions for classical problems in molecular design and synthesis planning. These new methods have the potential to fuel a new era of data-driven automation in scientific discovery. However, specialized models are still typically required for each task, leading to the need for problem-specific fine-tuning and neglecting task interrelations. The main obstacle in this field is the lack of a unified representation between natural language and chemical representations, complicating and limiting human-machine interaction. Here, we propose the first multi-domain, multi-task language model that can solve a wide range of tasks in both the chemical and natural language domains. Our model can handle chemical and natural language concurrently, without requiring expensive pre-training on single domains or task-specific models. Interestingly, sharing weights across domains remarkably improves our model when benchmarked against state-of-the-art baselines on single-domain and cross-domain tasks. In particular, sharing information across domains and tasks gives rise to large improvements in cross-domain tasks, the magnitude of which increase with scale, as measured by more than a dozen of relevant metrics. Our work suggests that such models can robustly and efficiently accelerate discovery in physical sciences by superseding problem-specific fine-tuning and enhancing human-model interactions. Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica |
ICML | 5 |
| 2020 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative ModelsabstractThe novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules for unseen viral targets, CogMol leverages a protein-molecule binding affinity predictor that is trained using SMILES VAE embeddings and protein sequence embeddings learned unsupervised from a large corpus. We applied the CogMol framework to three SARS-CoV-2 target proteins: main protease, receptor-binding domain of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both the molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87--95\% of high affinity molecules showed docking free energy $<$ -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low predicted parent molecule and metabolite toxicity and high predicted synthetic feasibility. In summary, CogMol can handle multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, even to novel protein target sequences, and does not need target-dependent fine-tuning of the framework or target structure information. Vijil Chenthamarakshan, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic |
NeurIPS | 10 |
| 2016 | Enhanced MPSM3 for applications to quantum biological simulationsabstractClassical molecular dynamics simulations have been the preferred method to cope with the characteristic sizes and time scales of complex life-science systems. However, while classical methods have well known limitations, such as that their accuracy strongly depends on empirical tuning, the practical use of far more accurate methods that rely on quantum Hamiltonians, has been limited by the current efficiency and scalability of sparse matrix-matrix multiplication algorithms used in the self-consistent field equations. In this paper, we show unprecedented massive scalability of a recently presented method, called MPSM3, for sparse matrix-matrix multiplication. The algorithmic basis of the method was presented in a recent publication, while here we describe the algorithmic enhancements that allow us to claim at least one order of magnitude improvement in scalability and time to solution over the state of the art (original MPSM3). We achieve a time to solution for the multiplication of density matrices within the self-consistent field scheme that is approaching the time needed to evaluate energy and forces with classical force-field methods and that is independent from the system size, provided proportional resources. This latest development renders the application of entirely quantum Hamiltonians to systems of several millions of atoms for extended molecular dynamics investigations feasible. A. Pozdneev, Valéry Weber, Teodoro Laino, Costas Bekas, Alessandro Curioni |
SC | 3 |
| 2014 | Shedding Light on Lithium/Air Batteries Using Millions of Threads on the BG/Q SupercomputerabstractIn this work, we present a novel parallelization scheme for a highly efficient evaluation of the Hartree-Fock exact exchange (HFX) in ab initio molecular dynamics simulations, specifically tailored for condensed phase simulations. Our developments allow one to achieve the necessary accuracy for the evaluation of the HFX in a highly controllable manner. We show here that our solutions can take great advantage of the latest trends in HPC platforms, such as extreme threading, short vector instructions and highly dimensional interconnection networks. Indeed, all these trends are evident in the IBM Blue Gene/Q supercomputer. We demonstrate an unprecedented scalability up to 6,291,456 threads (96 BG/Q racks) with a near perfect parallel efficiency, which represents a more than 20-fold improvement as compared to the current state of the art. In terms of reduction of time to solution, we achieved an improvement that can surpass a 10-fold decrease in runtime with respect to directly comparable approaches. We exploit this development to enhance the accuracy of DFT based molecular dynamics by using the PBE0 hybrid functional. This approach allowed us to investigate the chemical behavior of organic solvents in one of the most challenging research topics in energy storage, lithium/air batteries, and to propose alternative solvents with enhanced stability to ensure an appropriate reversible electrochemical reaction. This step is key for the development of a viable lithium/air storage technology, which would have been a daunting computational task using standard methods. Recent research has shown that the electrolyte plays a key role in non-aqueous lithium/air batteries in producing the appropriate reversible electrochemical reduction. In particular, the chemical degradation of propylene carbonate, the typical electrolyte used, by lithium peroxide has been demonstrated by molecular dynamics simulations of highly realistic models. Reaching the necessary high accuracy in these simulations is a daunting computational task using standard methods. Valéry Weber, Costas Bekas, Teodoro Laino, Alessandro Curioni, Adam Bertsch, Scott Futral |
IPDPS | 3 |