”Parallelized variable selection and modeling based on prediction” algorithm on GPU for feature selection and ADMET model generation

Koneti, Geervani; Ramamurthi, Narayanan

doi:10.1109/bibm.2017.8218020

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning has played an important role in virtual screening, which can better obtain molecule property by learning molecular representations, and the more molecular representations fed into deep neural networks, the more comprehensive molecular properties they learn. However, in real life, the number of discovered molecules with the same properties is often very little [10] . Guo et al applying few-shot learning to molecular property prediction effectively solves the problem of small amount of molecular data in reality.…”

Section: Introductionmentioning

confidence: 99%

Few-shot graph and smiles learning for molecular property prediction

Sun

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

Molecular property prediction can be applied to discover new drugs, which has attracted significant attention from both chemists and machine learning researchers. However, the existing methods for predicting molecular properties have the following limitations. The molecular representions learned from 2D structures will lose some important information contained in the molecular representions learned from 1D SMILES strings and vice versa; Second, the few-shot learning for molecular property prediction ignores the great differences between properties of different moleculars. To address the above problems, we propose a molecular property prediction model based on few-shot learning, called FewGS in this paper. We present a molecular graph-string combination method to fuse SMILES and graph represention, which can exploit a small amount of available molecular information to capture the hidden feature of moleculars. To promote the few-shot learning for molecular property prediction, we also propose a sample space transformation strategy to effectively eliminate bias between meta-train data and meta-test data. In addition, we construct a loss function based on graph-string combination, and adjust the weights through a self-attentive mechanism to achieve accurate prediction. We evaluate FewGS on molecular datasets and experimental results show that FewGS achieves state-of-the-art performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Few-shot graph and smiles learning for molecular property prediction

Sun

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

show abstract

”Parallelized variable selection and modeling based on prediction” algorithm on GPU for feature selection and ADMET model generation

Cited by 1 publication

References 2 publications

Few-shot graph and smiles learning for molecular property prediction

Few-shot graph and smiles learning for molecular property prediction

Contact Info

Product

Resources

About