| 1. |
- Genheden, Samuel, et al.
(författare)
-
Prediction of the Chemical Context for Buchwald-Hartwig Coupling Reactions
- 2022
-
Ingår i: Molecular Informatics. - : Wiley. - 1868-1751 .- 1868-1743. ; 41:8
-
Tidskriftsartikel (refereegranskat)abstract
- We present machine learning models for predicting the chemical context for Buchwald-Hartwig coupling reactions, i. e., what chemicals to add to the reactants to give a productive reaction. Using reaction data from in-house electronic lab notebooks, we train two models: one based on single-label data and one based on multi-label data. Both models show excellent top-3 accuracy of approximately 90 %, which suggests strong predictivity. Furthermore, there seems to be an advantage of including multi-label data because the multi-label model shows higher accuracy and better sensitivity for the individual contexts than the single-label model. Although the models are performant, we also show that such models need to be re-trained periodically as there is a strong temporal characteristic to the usage of different contexts. Therefore, a model trained on historical data will decrease in usefulness with time as newer and better contexts emerge and replace older ones. We hypothesize that such significant transitions in the context-usage will likely affect any model predicting chemical contexts trained on historical data. Consequently, training context prediction models warrants careful planning of what data is used for training and how often the model needs to be re-trained.
|
|
| 2. |
- Maertens, Jeroen, 1990, et al.
(författare)
-
Molecular-dynamics-simulation-guided membrane engineering allows the increase of membrane fatty acid chain length in Saccharomyces cerevisiae
- 2021
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- The use of lignocellulosic-based fermentation media will be a necessary part of the transition to a circular bio-economy. These media contain many inhibitors to microbial growth, including acetic acid. Under industrially relevant conditions, acetic acid enters the cell predominantly through passive diffusion across the plasma membrane. The lipid composition of the membrane determines the rate of uptake of acetic acid, and thicker, more rigid membranes impede passive diffusion. We hypothesized that the elongation of glycerophospholipid fatty acids would lead to thicker and more rigid membranes, reducing the influx of acetic acid. Molecular dynamics simulations were used to predict the changes in membrane properties. Heterologous expression of Arabidopsis thaliana genes fatty acid elongase 1 (FAE1) and glycerol-3-phosphate acyltransferase 5 (GPAT5) increased the average fatty acid chain length. However, this did not lead to a reduction in the net uptake rate of acetic acid. Despite successful strain engineering, the net uptake rate of acetic acid did not decrease. We suggest that changes in the relative abundance of certain membrane lipid headgroups could mitigate the effect of longer fatty acid chains, resulting in a higher net uptake rate of acetic acid.
|
|
| 3. |
- Westerlund, Annie M., et al.
(författare)
-
Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis
- 2024
-
Ingår i: Journal of Chemical Information and Modeling. - 1549-960X .- 1549-9596. ; 64:8, s. 3021-3033
-
Tidskriftsartikel (refereegranskat)abstract
- Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called the Chemformer, for retrosynthesis predictions within drug discovery. The proprietary data set used for training comprised ∼18 M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multistep retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to that of a template-based model. By analyzing multistep retrosynthesis experiments, we observed that Chemformer found synthetic routes, leading to commercial starting materials for 95% of the target compounds, an increase of more than 20% compared to the template-based model on a proprietary compound data set. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates, which are not included in the template-based model. These findings were further supported by a publicly available ChEMBL compound data set. The conclusions drawn from this work allow for the design of a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.
|
|
