Predictive modelling of the LD50 activities of coumarin derivatives using neural statistical approaches: Electronic descriptor-based DFT

Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions

Determining various properties of molecules is a critical step in drug discovery. Recently, with the improvement of large heterogeneous datasets and the development of deep learning approaches, more and more scientists have turned their attention to neural network-based virtual preliminary screening to reduce the time and monetary cost of drug discovery. However, the poor interpretability of deep learning masks causality, so models' conclusions are often beyond the comprehension of human users, which reduces the credibility of the model and makes it difficult for chemists to further narrow the huge chemical space based on models' results. Thus, this study develops a novel framework consisting of Graph Neural Networks for feature extraction, Curriculum-Based Learning Strategies for optimization, and a Learning Binary Neural Tree (LBNT) for prediction, to improve the performance of neural networks and reveal their decision-making process to chemists. The framework encodes molecular graph data with graph neural networks (GNNs), then retrains the encoder with curriculum-based learning strategies to reduce uncertainty and improve accuracy, and finally uses LBNT as the predictor, which joint retrains with the encoder after independently training, for prediction and visualization. The framework is validated on the public datasets and compared to single GNNs with normal training strategies as well as GNN encoders with common machine learning predictors instead of the LBNT predictor. The result reveals that the proposed framework enhances the point prediction accuracy of the completely trained GNN and reduces its uncertainty through curriculum-based learning, and further improves the accuracy by combining LBNT. Besides, compared with common machine learning tools, the LBNT predictor generally has the best performance because of joint retraining with the GNN encoder. The decision-making process of LBNT is also better and easier to explain than that of other models.

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation

In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.

Replacing libraries in scatterometry

Diffraction gratings have a wide array of applications in optics, diagnostics, food science, sensing, and process inspection. Scattering effects from defects can severely degrade the performance of such gratings. In this paper, we consider three classes of defects: Two classes introduced at the grating/air interface, as a change in line heights, and one class introduced as a sinusoidal variation of the grating/substrate interface. The scattering properties of the gratings are modelled using rigorous coupled wave analysis, and defects are approximated with a new semi-analytical model and a neural network. The new methods make it possible to avoid the time consuming library generation/search strategy commonly used in scatterometry. The method does not introduce new numerical parameters, and therefore no new parameter correlations. This work enables improved grating reconstruction, especially of non-diffracting short pitch gratings. It is found that two of the defect classes can be adequately described by the semi-analytical model, while the third defect is accurately reconstructed by a neural network. The network is demonstrated to be faster than a library search and more versatile for related structures.