Search

Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein–ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1% = 0.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline. Key points: A new scoring tool based on machine-learning was developed to predict PARP1 inhibitors for potential cancer treatment. The majority of PARP1-specific machine-learning models performed better than generic and classical scoring functions. Augmenting the training set with ligand-only Morgan fingerprint features generally resulted in better performing models, but not for the best models where no further improvement was observed. Employing protein-ligand-extracted fingerprints as molecular descriptors led to the best-performing and most-efficient model for predicting PARP1 inhibitors. Deep learning performed poorly on this target in comparison with the simpler ML models. [ABSTRACT FROM AUTHOR]

Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians. Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site. Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model. Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data. [ABSTRACT FROM AUTHOR]

Molecular docking can be used to predict how strongly small‐molecule binders and their chemical derivatives bind to a macromolecular target using its available three‐dimensional structures. Scoring functions (SFs) are employed to rank these molecules by their predicted binding affinity (potency). A classical SF assumes a predetermined theory‐inspired functional form for the relationship between the features characterizing the structure of the protein–ligand complex and its predicted binding affinity (this relationship is almost always assumed to be linear). Recent years have seen the prosperity of machine‐learning SFs, which are fast regression models built instead with contemporary supervised learning algorithms. In this review, we analyzed machine‐learning SFs for drug lead optimization in the 2015–2019 period. The performance gap between classical and machine‐learning SFs was large and has now broadened owing to methodological improvements and the availability of more training data. Against the expectations of many experts, SFs employing deep learning techniques were not always more predictive than those based on more established machine learning techniques and, when they were, the performance gain was small. More codes and webservers are available and ready to be applied to prospective structure‐based drug lead optimization studies. These have exhibited excellent predictive accuracy in compelling retrospective tests, outperforming in some cases much more computationally demanding molecular simulation‐based methods. A discussion of future work completes this review. This article is categorized under:Computer and Information Science > Chemoinformatics [ABSTRACT FROM AUTHOR]

Recent advances in deep learning methods have redefined the state-of-the-art for many medical imaging applications, surpassing previous approaches and sometimes even competing with human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabeled data. • Deep Learning models suffer from poor generalization when applied to other centers. • Unsupervised domain adaptation can mitigate this issue. • Here we show that self-ensembling technique shows better performance even with small amount of training data. • Ablation study demonstrates that unlabeled data provides significant improvements. [ABSTRACT FROM AUTHOR]