Deep learning research, from ResNet to AlphaFold2, convincingly shows that deep learning can predict the native conformation of a given protein sequence with high accu- racy. Accounting for the plasticity of protein molecules remains challenging, and powerful algorithms are needed to sample the conformation space of a given amino-acid sequence. In the complex and high-dimensional energy surface that accompanies this space, it is critical to explore a broad range of areas. In this paper, we present a novel evolutionary algorithm that guides its optimization process with a memory of the explored conformation space, so that it can avoid searching already explored regions and search in the unexplored regions. The algorithm periodically consults an evolving map that stores already sampled non- redundant conformations to enhance exploration during selection. Evaluation on diverse datasets shows superior performance of the algorithm over the state-of-the-art algorithms.
Aliphatic-cored di-1,2,4-triazole ligands form both a metal–organic nanotube (MONT) and a coordination polymer upon reaction with silver nitrate.
more » « less- Award ID(s):
- 2207224
- NSF-PAR ID:
- 10519842
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- CrystEngComm
- Volume:
- 25
- Issue:
- 35
- ISSN:
- 1466-8033
- Page Range / eLocation ID:
- 4929 to 4933
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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