Dataset Information

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

ABSTRACT: Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.

SUBMITTER: Points LJ

PROVIDER: S-EPMC5798325 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

Points Laurie J LJ Taylor James Ward JW Grizou Jonathan J Donkers Kevin K Cronin Leroy L

Proceedings of the National Academy of Sciences of the United States of America 20180116 5

Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances ...[more]

PMID: 29339510

Similar Datasets

Project description:Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deep-learning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligand-binding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds' mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.

Project description:Tuberculosis (TB), entrained by Mycobacterium tuberculosis, continues to be an enfeebling disease, killing nearly 1.5 million people in 2019, with 2 billion people worldwide affected by latent TB. The multidrug-resistant and totally drug-resistant emerging strains further exacerbate the TB infection. The cell wall of bacteria provides critical virulence components such as cell surface proteins, regulators, signal transduction proteins, and toxins. The cell wall biosynthesis pathway of Mycobacterium tuberculosis is exhaustively studied to discover novel drug targets. Decaprenylphosphoryl-β-d-ribose-2'-epimerase (DprE1) is an important enzyme involved in the arabinogalactan biosynthetic pathway of Mycobacterium tuberculosis cell wall and is essential for both latent and persistent bacterial infection. We analyzed all known ∼1300 DprE1 inhibitors to gain deep insights into the chemogenomic space of DprE1-ligand complexes. Physicochemical descriptors of the DprE1 inhibitors showed a marked lipophilic character forming a cluster distinct from the existing TB drugs, as revealed by the principal component analysis. Similarity analysis using Murcko scaffolds and rubber band scaling revealed scarce representation of the chemical space. Further, Murcko scaffold analysis uncovered favorable and unfavorable scaffolds, where benzo and pyridine-based core scaffolds exhibit the highest biological activity, as evidenced by their MIC and IC50 values. Automatic SAR and R-group decomposition analysis resulted in the identification of substructures responsible for the inhibitory activity of the DprE1 enzyme. Further, with activity cliff analysis, we observed prominent discontinuity in the SAR of DprE1 inhibitors, where even simple structural modification in the chemical scaffold resulted in significant potency difference, presumably due to the binding orientation and interaction in the active site. Thiophene, 6-membered aromatic rings, and unsubstituted benzene ring-based toxicophores were identified in the DprE1 chemical space using an artificial intelligence approach based on inductive logic programming. This paper, hence, ushers in new insights for the design and development of potent covalent and non-covalent DprE1 inhibitors and guides hit and lead optimization for the development of non-hazardous small molecule therapeutics for Mycobacterium tuberculosis.

Project description:BackgroundChatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet.ObjectiveThe purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations.MethodsWe conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model.ResultsOur review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper.ConclusionsAs AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot's relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.

Dataset Information

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

Publications

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets