Search Results for author: Teodoro Laino

Found 13 papers, 6 papers with code

Language models in molecular discovery

no code implementations28 Sep 2023 Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan, Teodoro Laino, Jannis Born

The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers.

Chatbot Drug Discovery +2

Unifying Molecular and Textual Representations via Multi-task Language Modelling

1 code implementation29 Jan 2023 Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica

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.

Language Modelling Molecule Captioning +2

Identification of Enzymatic Active Sites with Unsupervised Language Modeling

no code implementations NeurIPS Workshop AI4Scien 2021 Loïc Kwate Dassi, Matteo Manica, Daniel Probst, Philippe Schwaller, Yves Gaetan Nana Teukam, Teodoro Laino

Herein, we apply a Transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions.

Language Modelling

Unassisted Noise Reduction of Chemical Reaction Data Sets

1 code implementation2 Feb 2021 Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino

Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones).

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

no code implementations NeurIPS 2020 Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

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.

Attribute Retrosynthesis +1

Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery

no code implementations10 Feb 2020 Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Ozkirimli

Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.

Drug Discovery

Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy

no code implementations17 Oct 2019 Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, Teodoro Laino

We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.


An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries

no code implementations19 Jul 2019 Matteo Manica, Christoph Auer, Valery Weber, Federico Zipoli, Michele Dolfi, Peter Staar, Teodoro Laino, Costas Bekas, Akihiro Fujita, Hiroki Toda, Shuichi Hirose, Yasumitsu Orii

Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.

Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials

1 code implementation8 Jan 2019 Tobias Binninger, Aris Marcolongo, Matthieu Mottet, Valéry Weber, Teodoro Laino

Superior stability and safety are key promises attributed to all-solid-state batteries (ASSBs) containing solid-state electrolyte (SSE) compared to their conventional counterparts utilizing liquid electrolyte.

Materials Science Chemical Physics

"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models

1 code implementation13 Nov 2017 Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, Teodoro Laino

With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy.


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