Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years.

Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical and chemical properties and molecular fingerprints in statistical models and classical machine learning to advanced deep learning approaches. In this review, we aim to distill insights from current research on employing transformer models for MPP.

A Y374X TDP43 truncation leads to an altered metabolic profile in amyotrophic lateral sclerosis fibroblasts driven by pyruvate and TCA cycle intermediate alterations.

A p.Y374X truncation in TARDBP was recently shown to reduce expression of TDP43 in fibroblasts isolated from ALS cases. In this follow up study focused on assessing the downstream phenotypic consequences of loss of TDP43 in the context of the truncation, we have shown a striking effect on the fibroblast metabolic profile.

Dysregulation of cholesterol homeostasis in human lung cancer tissue and tumour-associated macrophages.

Background: Based on reports on elevated cholesterol levels in cancer cells, strategies to lower cholesterol synthesis have been suggested as an antitumour strategy. However, cholesterol depletion has also been shown to induce tumour-promoting actions in tumour-associated macrophages (TAMs).

Methods: We performed lipidomic and transcriptomic analyses of human lung cancer material.

Combined QM/MM and Monte Carlo study for redox leveling in Mn and Fe superoxide dismutase.

Superoxide dismutases (SOD) are vital enzymes for disproportionation of superoxide molecules in mammals. Despite the high similarity between the Mn-SOD and Fe-SOD, they are inactive if the metals in the active sites are exchanged. Here, we use DFT, QM/MM and Monte Carlo sampling to optimize the crystal structure and to calculate the mid-point potential for the native and substituted Mn/Fe-SOD.