Search Results for author:
Found 13 papers, 7 papers with code
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Here, we propose a multi-domain, multi-task language model to solve a wide range of tasks in both the chemical and natural language domains.
Domain-agnostic and Multi-level Evaluation of Generative Models
To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
Regression Transformer: Concurrent sequence regression and generation for molecular language modeling
To that end, we propose the Regression Transformer (RT), a novel method that abstracts regression as a conditional sequence modeling problem.
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks
Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance.
On the Importance of Looking at the Manifold
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.
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools.
PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents.
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
For detecting COVID-19 in particular, the model performs with a sensitivity of 0. 96, a specificity of 0. 79 and F1-score of 0. 92 in a 5-fold cross validation.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
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
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).
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
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
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.
