Arrow Research search

Author name cluster

Cesar Berrospi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

AAAI Conference 2024 System Paper

ESG Accountability Made Easy: DocQA at Your Service

  • Lokesh Mishra
  • Cesar Berrospi
  • Kasper Dinkla
  • Diego Antognini
  • Francesco Fusco
  • Benedikt Bothur
  • Maksym Lysak
  • Nikolaos Livathinos

We present Deep Search DocQA. This application enables information extraction from documents via a question-answering conversational assistant. The system integrates several technologies from different AI disciplines consisting of document conversion to machine-readable format (via computer vision), finding relevant data (via natural language processing), and formulating an eloquent response (via large language models). Users can explore over 10,000 Environmental, Social, and Governance (ESG) disclosure reports from over 2000 corporations. The Deep Search platform can be accessed at: https://ds4sd.github.io.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Knowledge Enhanced Representation Learning for Drug Discovery

  • Thanh Lam Hoang
  • Marco Luca Sbodio
  • Marcos Martinez Galindo
  • Mykhaylo Zayats
  • Raul Fernandez-Diaz
  • Victor Valls
  • Gabriele Picco
  • Cesar Berrospi

Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences. While these representations have significantly enhanced the predictions, they are usually based on a limited set of modalities, and they do not exploit available knowledge about existing relations among molecules and proteins. Our study reveals that enhanced representations, derived from multimodal knowledge graphs describing relations among molecules and proteins, lead to state-of-the-art results in well-established benchmarks (first place in the leaderboard for Therapeutics Data Commons benchmark ``Drug-Target Interaction Domain Generalization Benchmark", with an improvement of 8 points with respect to previous best result). Moreover, our results significantly surpass those achieved in standard benchmarks by using conventional pre-trained representations that rely only on sequence or SMILES data. We release our multimodal knowledge graphs, integrating data from seven public data sources, and which contain over 30 million triples. Pretrained models from our proposed graphs and benchmark task source code are also released.

PDF Details DOI
v2026.09.13