乐胖代购免代理版

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To solve the above problems, an end-to-end double-head transformer neural network (DHTNN) is proposed in this paper for high-precision molecular property prediction. For the data distribution characteristics of the molecular dataset, DHTNN specially designs a new activation function, beaf, which can greatly improve the generalization ability of the nonlinear representation of molecular features. A residual network is introduced in the molecular encoding part to solve the gradient explosion problem and ensure that the model can converge quickly. The transformer based on double-head attention is used to extract molecular intrinsic detail features, and the weights are reasonably assigned for predicting molecular properties with high accuracy. Our model, which was tested on the MoleculeNet [1] benchmark dataset, showed significant performance improvements over other state-of-the-art methods.<\/jats:p>","DOI":"10.1186\/s13321-023-00700-4","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T14:04:49Z","timestamp":1677161089000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Double-head transformer neural network for molecular property prediction"],"prefix":"10.1186","volume":"15","author":[{"given":"Yuanbing","family":"Song","sequence":"first","affiliation":[]},{"given":"Jinghua","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Wenju","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhichong","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"issue":"2","key":"700_CR1","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) Moleculenet: a benchmark for molecular machine learning. 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