Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets
  and Contrastive Molecular Explanations   Explainable ML for molecular toxicity prediction is a promising approach for
efficient drug development and chemical safety. A predictive ML model of
toxicity can reduce experimental cost and time while mitigating ethical
concerns by significantly reducing animal and clinical testing. Herein, we use
a deep learning framework for simultaneously modeling in vitro, in vivo, and
clinical toxicity data. Two different molecular input representations are used:
Morgan fingerprints and pre-training SMILES embeddings. A multi-task deep
learning model accurately predicts toxicity for all endpoints, including
clinical, as indicated by AUROC and balanced accuracy. In particular, SMILES
embeddings as input to the multi-task model improved clinical toxicity
predictions compared to existing models in MoleculeNet benchmark. Additionally,
our multi-task approach is comprehensive in the sense that it is comparable to
state-of-the-art approaches for specific endpoints in in vitro, in vivo and
clinical platforms. Through both the multi-task model and transfer learning, we
were able to indicate the minimal need of in vivo data for clinical toxicity
predictions. To provide confidence and explain the model's predictions, we
adapt a post-hoc contrastive explanation method that returns pertinent positive
and pertinent negative features, which correspond well to known mutagenic and
reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic
amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent
feature analysis captures more of the in vitro (53%) and in vivo (56%), rather
than of the clinical (8%), endpoints, and indeed uncovers a preference in known
toxicophore data towards in vitro and in vivo experimental data. To our
knowledge, this is the first contrastive explanation, using both present and
absent substructures, for predictions of clinical and in vivo molecular
toxicity.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an