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2018 ; 37
(1-2
): ä Nephropedia Template TP
gab.com Text
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Generative Recurrent Networks for De Novo Drug Design
#MMPMID29095571
Gupta A
; Müller AT
; Huisman BJH
; Fuchs JA
; Schneider P
; Schneider G
Mol Inform
2018[Jan]; 37
(1-2
): ä PMID29095571
show ga
Generative artificial intelligence models present a fresh approach to
chemogenomics and de novo drug design, as they provide researchers with the
ability to narrow down their search of the chemical space and focus on regions of
interest. We present a method for molecular de novo design that utilizes
generative recurrent neural networks (RNN) containing long short-term memory
(LSTM) cells. This computational model captured the syntax of molecular
representation in terms of SMILES strings with close to perfect accuracy. The
learned pattern probabilities can be used for de novo SMILES generation. This
molecular design concept eliminates the need for virtual compound library
enumeration. By employing transfer learning, we fine-tuned the RNN's predictions
for specific molecular targets. This approach enables virtual compound design
without requiring secondary or external activity prediction, which could
introduce error or unwanted bias. The results obtained advocate this generative
RNN-LSTM system for high-impact use cases, such as low-data drug discovery,
fragment based molecular design, and hit-to-lead optimization for diverse drug
targets.
free
free
free
