Search Results for author: Matteo Manica

Found 20 papers, 7 papers with code

Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens

1 code implementation1 Feb 2022 Jannis Born, Matteo Manica

We report the Regression Transformer (RT), a method that abstracts regression as a conditional sequence modeling problem.

Conditional Text Generation

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

On the Importance of Looking at the Manifold

no code implementations1 Jan 2021 Nil Adell Mill, Jannis Born, Nathaniel Park, James Hedrick, María Rodríguez Martínez, Matteo Manica

We explore a spectrum of models, ranging from uniquely learning representations based on the isolated features of the nodes (focusing on Variational Autoencoders), to uniquely learning representations based on the topology (using node2vec) passing through models that integrate both node features and topological information in a hybrid fashion.

Representation Learning

Understood in Translation, Transformers for Domain Understanding

1 code implementation18 Dec 2020 Dimitrios Christofidellis, Matteo Manica, Leonidas Georgopoulos, Hans Vandierendonck

Focusing on scientific document understanding, we present a new health domain dataset based on publications extracted from PubMed and we successfully utilize our method on this.


PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models

1 code implementation27 May 2020 Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents.

Drug Discovery

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.

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning

no code implementations29 Aug 2019 Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Karsten Borgwardt, María Rodríguez Martínez

The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).


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.

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

1 code implementation25 Apr 2019 Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder.

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

1 code implementation16 Nov 2018 Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

Our models ingest a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value.

Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer

no code implementations18 Aug 2018 Ali Oskooei, Matteo Manica, Roland Mathis, Maria Rodriguez Martinez

We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data.

Mixed-Precision In-Memory Computing

no code implementations16 Jan 2017 Manuel Le Gallo, Abu Sebastian, Roland Mathis, Matteo Manica, Heiner Giefers, Tomas Tuma, Costas Bekas, Alessandro Curioni, Evangelos Eleftheriou

As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers.

Emerging Technologies

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