Molecular Design With Long Short-Term Memory Networks

被引：2
|
作者：
Grisoni, Francesca [1 ]
Schneider, Gisbert [1 ]
机构：
[1] Swiss Fed Inst Technol, RETHINK, Dept Chem & Appl Biosci, Vladimir Prelog Weg 4, CH-8093 Zurich, Switzerland
来源：
JOURNAL OF COMPUTER AIDED CHEMISTRY | 2019年 / 20卷
关键词：
recurrent neural network (RNN); long short-term memory network (LSTM); de novo design; transfer learning; DATABASE; IDENTIFICATION; GENERATION; AGONIST; SMILES;
D O I：
10.2751/jcac.20.35
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Computer-assisted de novo drug design has been a central research topic in the field of chemoinformatics for approximately 30 years. Professor Kimito Funatsu's research has been a formative component in these developments. His seminal work has contributed inverse quantitative-structure-activity relationship (QSAR) models for small molecule and peptide design. This article highlights a class of recurrent neural networks, so-called long short-term memory (LSTM) networks for generative molecular design, which further the conceptual approach of inverse QSAR. We review the LSTM method for molecular design along with selected practical applications.
引用
收藏
页码：35 / 42
页数：8
相关论文
共 50 条
  • [1] On the Initialization of Long Short-Term Memory Networks
    Ghazi, Mostafa Mehdipour
    Nielsen, Mads
    Pai, Akshay
    Modat, Marc
    Cardoso, M. Jorge
    Ourselin, Sebastien
    Sorensen, Lauge
    NEURAL INFORMATION PROCESSING (ICONIP 2019), PT I, 2019, 11953 : 275 - 286
  • [2] Evolving Long Short-Term Memory Networks
    Neto, Vicente Coelho Lobo
    Passos, Leandro Aparecido
    Papa, Joao Paulo
    COMPUTATIONAL SCIENCE - ICCS 2020, PT II, 2020, 12138 : 337 - 350
  • [3] De novo Molecular Design with Generative Long Short-term Memory
    Grisoni, Francesca
    Schneider, Gisbert
    CHIMIA, 2019, 73 (12) : 1006 - 1011
  • [4] Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
    Abbas, Zainab
    Al-Shishtawy, Ahmad
    Girdzijauskas, Sarunas
    Vlassov, Vladimir
    2018 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS), 2018, : 57 - 65
  • [5] Diagnosing Dysarthria with Long Short-Term Memory Networks
    Mayle, Alex
    Mou, Zhiwei
    Bunescu, Razvan
    Mirshekarian, Sadegh
    Xu, Li
    Liu, Chang
    INTERSPEECH 2019, 2019, : 4514 - 4518
  • [6] Long Short Term Memory Networks for Short-Term Electric Load Forecasting
    Narayan, Apurva
    Hipel, Keith W.
    2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2017, : 2573 - 2578
  • [7] Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks
    Wang, Bipeng
    Winkler, Ludwig
    Wu, Yifan
    Muller, Klaus-Robert
    Sauceda, Huziel E.
    Prezhdo, Oleg V.
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2023, 14 (31): : 7092 - 7099
  • [8] ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE
    Dogan, Erdem
    SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT, 2020, 107 : 19 - 32
  • [9] Long short-term memory
    Hochreiter, S
    Schmidhuber, J
    NEURAL COMPUTATION, 1997, 9 (08) : 1735 - 1780
  • [10] Conditioning and time representation in long short-term memory networks
    Francois Rivest
    John F. Kalaska
    Yoshua Bengio
    Biological Cybernetics, 2014, 108 : 23 - 48
12345