Modeling Industrial ADMET Data with Multitask Networks
Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications. Using a set of industrial ADMET datasets, we compare neural networks to standard baseline models and analyze multitask learning effects with both random cross-validation and a more relevant temporal validation scheme. We confirm that multitask learning can provide modest benefits over single-task models and show that smaller datasets tend to benefit more than larger datasets from multitask learning. Additionally, we find that adding massive amounts of side information is not guaranteed to improve performance relative to simpler multitask learning. Our results emphasize that multitask effects are highly dataset-dependent, suggesting the use of dataset-specific models to maximize overall performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoverySimilar Papers 제목 키워드 기반
Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
Chemical pretrained models, sometimes referred to as foundation models, are receiving considerable interest for drug discovery applications. The general chemical knowledge extracted from self-supervised training has the …
Graph Neural NetworkMulti-Task LearningDrug DiscoveryChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction
Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors use undirected molecular graphs and pairwise edges. This choice misses asymmet…
Drug DiscoveryHelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent year…
Drug DiscoverySelf-Supervised LearningTransfer LearningAccurate ADMET Prediction with XGBoost
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety. In this work, we applied an ensemble of features, including fingerp…
Drug DiscoveryMolecular Property PredictionPredictionTDC ADMET Benchmarking Group+1Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction
Machine learning (ML) has been playing important roles in drug discovery in the past years by providing (pre-)screening tools for prioritising chemical compounds to pass through wet lab experiments. One of the main ML ta…
AutoMLComputational chemistryDrug DiscoveryProperty Prediction