Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties.

Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.

Habitat-Specific Clock Variation and Its Consequence on Reproductive Fitness.

The circadian clock controls daily activities at the cellular and organismic level, allowing an organism to anticipate incoming stresses and to use resources accordingly. The circadian clock has therefore been considered a fitness trait in multiple organisms. However, the mechanism of how circadian clock variation influences organismal reproductive fitness is still not well understood. Here we describe habitat-specific clock variation (HSCV) of asexual reproduction in Neurospora discreta, a species that is adapted to 2 different habitats, under or above tree bark. African (AF) N. discreta strains, whose habitat is above the tree bark in light-dark (LD) conditions, display a higher rhythmicity index compared with North American (NA) strains, whose habitat is under the tree bark in constant dark (DD). Although AF-type strains demonstrated an overall fitness advantage under LD and DD conditions, NA-type strains exhibit a habitat-specific fitness advantage in DD over the LD condition. In addition, we show that allelic variation of the clock-controlled gene, Ubiquinol cytochrome c oxidoreductase (NEUDI_158280), plays a role in HSCV by modulating cellular reactive oxygen species levels. Our results demonstrate a mechanism by which local adaptation involving circadian clock regulation influences reproductive fitness.

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.

BACKGROUND: Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data. OBJECTIVE: We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach. METHODS: A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity. RESULTS: Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62-100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment -in vitro-in vivo relationships were identified to illustrate new animal toxicity mechanisms. CONCLUSIONS: The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614.

CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data.

Summary: We have developed a public Chemical In vitro­In vivo Profiling (CIIPro) portal, which can automatically extract in vitro biological data from public resources (i.e. PubChem) for user-supplied compounds. For compounds with in vivo target activity data (e.g. animal toxicity testing results), the integrated cheminformatics algorithm will optimize the extracted biological data using in vitro­in vivo correlations. The resulting in vitro biological data for target compounds can be used for read-across risk assessment of target compounds. Additionally, the CIIPro portal can identify the most similar compounds based on their optimized bioprofiles. The CIIPro portal provides new powerful assessment capabilities to the scientific community and can be easily integrated with other cheminformatics tools. Availability and Implementation: ciipro.rutgers.edu. Contact: danrusso@scarletmail.rutgers.edu or hao.zhu99@rutgers.edu