Artificial intelligence in the life sciences最新文献

The Bioconcentration Factor (BCF) is an important parameter in the environmental risk assessment of chemicals, relevant for industrial and academic research as well as required in many regulatory contexts. It represents the potential of a substance to accumulate in organic tissues or whole animals and is most frequently measured in fish. However, animal welfare reasons, throughput limitations, and costs push the need for alternative methods that allow accurate and reliable estimations of BCF in silico. We present a new deep learning model to predict BCF values from chemical structures, that outperforms currently available models ($R^2$ of 0.68 and RMSE of 0.59 log units on an external test set; $R^2$ of 0.70 and RMSE of 0.74 log units in a demanding cluster split validation). The model is based on molecular representations encoded as CDDD descriptors and exploits a large in-house dataset with measured logD values as an auxiliary task.

Additionally, we developed a post-hoc explainability method based on SMILES character substitutions to accompany our predictions with atom-level interpretations. These sensitivity scores highlight the most influential moieties in the molecule and can help to understand the predictions better and design new molecules.

The interpretation of quantitative structure–activity or structure–property relationships is important in the field of chemoinformatics. Although multivariate linear regression models are typically interpretable, they do not generally have high predictive abilities. Symbolic regression (SR) combined with genetic programming (GP) is a well-established technique for generating the mathematical expressions that describe the relationships within a dataset. However, SR sometimes produces complicated expressions that are hard for humans to interpret. This paper proposes a method for generating simpler expressions by incorporating three filters into GP-based SR. The filters are further combined with nonlinear least-squares optimization to give filter-introduced GP (FIGP), which improves the predictive ability of SR models while retaining simple expressions. As a proof-of-concept, the quantitative estimate of drug-likeness and the synthetic accessibility score are predicted based on the chemical structures of compounds. Overall, FIGP generates less-complicated expressions than previous SR methods. In terms of predictive ability, FIGP is better than GP, but is outperformed by a support vector machine with a radial basis function kernel. Furthermore, quantitative structure–activity relationship models are constructed for three matching molecular series with biological targets. In the case of one target, the activity prediction models given by FIGP exhibit better predictive ability than multivariate linear regression and support vector regression with the radial basis function kernel, whereas for the remaining cases, FIGP is slightly less accurate than multivariate linear regression.

Computational linear T-cell epitope prediction tools allow cost and labor reduction in downstream in vitro testing, but the quality of currently available methods is compromised by the scarcity of experimental data and extensive HLA polymorphism. However, it is possible to improve prediction quality by forgoing HLA-dependency that allows treating all immunogenic sequences as a single group. This reduces the problem to a much simpler two-classes classification of determining whether a peptide is immunogenic or not. Here, we use a deep convolutional neural network capable of predicting linear T-cell epitope regions in primary structures trained using all peptides deposited in the IEDB website. We also investigate the possibility of using peptides derived from known human proteins as non-immunogenic counterexamples. We compared our model with a state-of-the-art tool and analyze the benefits of using larger databases. Our results corroborate the usefulness of HLA-free methods for practical applications that require the identification of immunogenic sequences. Deepitope is an open source project that can be found at https://github.com/raphaeltrevizani/deepitope.

Recent methodological advances in deep learning (DL) architectures have not only improved the performance of predictive models but also enhanced their interpretability potential, thus considerably increasing their transparency. In the context of medicinal chemistry, the potential to not only accurately predict molecular properties, but also chemically interpret them, would be strongly preferred. Previously, we developed accurate multi-task convolutional neural network (CNN) and graph convolutional neural network (GCNN) models to predict a set of diverse intrinsic metabolic clearance parameters from image- and graph-based molecular representations, respectively. Herein, we introduce several model interpretability frameworks to answer whether the model explanations obtained from CNN and GCNN multi-task clearance models could be applied to predict chemical transformations associated with experimentally confirmed metabolic products. We show a strong correlation between the CNN pixel intensities and corresponding clearance predictions, as well as their robustness to different molecular orientations. Using actual case examples, we demonstrate that both CNN and GCNN interpretations frequently complement each other, suggesting their high potential for combined use in guiding medicinal chemistry design.

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab-based experiments being performed, or impact on other decisions, incurring significant time and financial costs and most importantly, ultimately influencing patient healthcare. For KGE models to have impact in this domain, a better understanding of not only of performance, but also the various factors which determine it, is required.

In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our goal is not to focus on the best overall model or configuration, instead we take a deeper look at how performance can be affected by changes in the training setup, choice of hyperparameters, model parameter initialisation seed and different splits of the datasets. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models. Indeed these factors should be reported along with model architectures to ensure complete reproducibility and fair comparisons of future work, and we argue this is critical for the acceptance of use, and impact of KGEs in a biomedical setting.

There have been few efforts made to automate the cytomorphological categorization of bone marrow cells. For bone marrow cell categorization, deep-learning algorithms have been limited to a small number of samples or disease classifications. In this paper, we proposed a pipeline to classify the bone marrow cells despite these limitations. Data augmentation was used throughout the data to resolve any class imbalances. Then, random transformations such as rotating between 0${}^{\circ }$ to 90${}^{\circ }$, zooming in/out, flipping horizontally and/or vertically, and translating were performed. The model used in the pipeline was a CoAtNet and that was compared with two baseline models, EfficientNetV2 and ResNext50. We then analyzed the CoAtNet model using SmoothGrad and Grad-CAM, two recently developed algorithms that have been shown to meet the fundamental requirements for explainability methods. After evaluating all three models’ performance for each of the distinct morphological classes, the proposed CoAtNet model was able to outperform the EfficientNetV2 and ResNext50 models due to its attention network property that increased the learning curve for the algorithm which was represented using a precision-recall curve.