New LOLS machine learning approach facilitates molecular conformer search in complex molecules
CEST researchers developed a new machine learning approach based on a low-energy latent space (LOLS) and density functional theory (DFT) to search for molecular conformers.
Molecular conformer search is a topic of great importance in computational chemistry, drug design and material science. The challenge is to identify low-energy conformers in the first place. This difficulty arises from the high complexity of search spaces, as well as the computational cost associated with accurate quantum chemical methods. In the past, conformer search would take up considerable time and computational resources.
To address this challenge, visiting doctoral student Xiaomi Guo, together with other CEST researchers Lincan Fang, Prof. Patrick Rinke, Dr. Xi Chen, and Prof. Milica Todorovic (University of Turku) explored the possibility of performing the molecular conformer search in a low-dimensional latent space. This method uses a generative model variational auto-encoder (VAE) and biases the VAE towards low-energy molecular configurations to generate more informative data. In this way, the model can effectively learn the low-energy potential surface and hence identify the related molecular conformers. The CEST teams calls their new method low-energy latent space (LOLS) conformer search.
In a recent publication the authors tested this new LOLS procedure on amino acids and peptides with 5–9 searching dimensions. The new results agree well with previous studies. The team found that for small molecules such as cysteine, it is more efficient to sample data in real space; however, LOLS turns out to be more suitable for larger molecules such as peptides. The authors now plan to extend their structure search methods to more complex materials beyond molecules.
This research paper is published in The Journal of Chemical Theory and Computation under https://doi.org/10.1021/acs.jctc.2c00290.
The code used in this work can be found at https://github.com/gxm11/lols.
For more details contact
Xi Chen
Read more news
Semiconductor sector summer jobs open in research groups
The growth of the semiconductor sector and its investments in Finland are also increasing the need for skilled personnel. Thanks to a donation from four semiconductor companies Picosun, an Applied Materials Company, Okmetic, Murata, and KYOCERA Tikitin. Aalto University will offer exciting summer jobs in the School of Electrical Engineering and the School of Chemical Engineering in the summer of 2023. Semi-Summer 2023 Programme will provide an opportunity to gain the skills needed in a growing and international field.
Sakari Poikkimäki is replacing harmful chemicals with enzymes: ‘We can use small biological organisms in many everyday products’
Chemical engineering alumnus Sakari Poikkimäki works as a fermentation expert at Roal Oy, a company that manufactures enzymes. Studies at Aalto University School of Chemical Engineering lead to a dream job where small living organisms are harnessed for good.
Iita Kejonen is involved in recycling battery metals from electric cars: ‘By working in industry, I can influence the development of the entire field’
Studies in materials science and engineering at the School of Chemical Engineering led to work with battery metals and minerals. As a Technology Manager, Iita can influence the development of responsibility in the entire automotive sector.