. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

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Climate change, increasing populations, competing demands on land for production of biofuels, and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, whereby farmers respond in real-time to changes in crop growth, with nanotechnology and artificial intelligence offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery nutrient capture and long term impacts on soil microbial communities will allow design of nanoscale agrochemcials that combine optimal safety and functionality profiles. bacterial community structure after just 90 days of exposure to a realistic concentration of NPs (1 mg kg −1 dry soil) 10 , while studies with Ag NMs, which are well-known for their antimicrobial activity have shown that the extent of impact on soil community composition over 90 days are affected by exposure time and physicochemical composition of soil as well as the type and coating of the NMs 11 . Thus, an important caveat at the outset of this review is that NMs represent a very broad spectrum of chemistries, compositions and physicochemical properties, which are dynamic and evolving as the NMs interact with their surroundings, and as such generalisations regarding their applications in agriculture are difficult, and predictions of long-term effects are challenging currently.However, as noted in the aforementioned reviews 3, 4, 5 , the development of nanotechnology for agricultural applications is still at an early stage and is moving forward quite slowly. Significant differences may exist between nanotechnology-based pesticides and conventional pesticides, including altered bioavailability, sensitivity, dosimetry, and pharmacokinetics 12, 13 . Challenges and barriers include limited understanding of plant-NMs interactions, limited methods for efficient delivery of NMs to plants and soil, risks of potentially hazardous effects of NMs to human health from accumulation of NMs and active ingredient residues in edible portions of plants 4 , and to long term soil quality and soil health from accumulation of NMs and their degradation products in soil and resultant potential alterations in microbial biodiversity 14 . There is an urgent need to address these barriers and achieve a true win-win scenario, whereby improved agricultural production, reduced environmental pollution from agriculture and lower costs for farmers can be achieved synergistically. A one-health approach to nano-agriculture was proposed by Lombi et al., that requires interdisciplinarity and the bridging of human and environmental health research 15 . Computational approaches including artificial intelligence (A.I.) and machine learning (M.L.) modelling will undoubtedly play critical roles in the progess of nano-enabled agric...

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

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