The construction of multirate rearrangeable network has long been an interesting problem. Of many results published, all were achieved on 3-stage Clos network. The monotone routing algorithm proposed by Hu et al.(2001...The construction of multirate rearrangeable network has long been an interesting problem. Of many results published, all were achieved on 3-stage Clos network. The monotone routing algorithm proposed by Hu et al.(2001) was also first applied to 3-stage Clos network. In this work, we adopt this algorithm and apply it to logd(N,m,p) networks. We first analyze the properties of logd(N,m,p) networks. Then we use monotone algorithm in logd(N,0,p) network. Furthermore we extend the result to construct multirate rearrangeable networks based on logd(N,m,p) network (1≤m≤n?1).展开更多
Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to opti...Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.展开更多
基金Project (No. 10371028) supported by the National Natural ScienceFoundation of China
文摘The construction of multirate rearrangeable network has long been an interesting problem. Of many results published, all were achieved on 3-stage Clos network. The monotone routing algorithm proposed by Hu et al.(2001) was also first applied to 3-stage Clos network. In this work, we adopt this algorithm and apply it to logd(N,m,p) networks. We first analyze the properties of logd(N,m,p) networks. Then we use monotone algorithm in logd(N,0,p) network. Furthermore we extend the result to construct multirate rearrangeable networks based on logd(N,m,p) network (1≤m≤n?1).
基金This work was supported by the National Natural Science Foundation of China(Nos.62272288,61972451,and U22A2041)the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001).
文摘Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.
