563 citations · 1.3k across the 16 of their papers we have counts for
4 papers · 1 filter
PaccMann: Designing anticancer drugs from transcriptomic data via reinforcement learning
Jannis Born, Matteo Manica, Ali Oskooei +3
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep le…
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Matteo Manica, Christoph Auer, Valery Weber +9
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
Matteo Manica, Ali Oskooei, Jannis Born +3
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…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…