3DStructGen: an interactive web-based 3D structure generation for non-periodic molecule and crystal

Chen, Pin; Wang, Yu; Yan, Hui; Gao, Sen; Xu, Zexin; Li, Yangzhong; Mo, Qing; Huang, Junkang; Tao, Jun; Pan, GeChuanqi; Li, Jiahui; Du, Yunfei

doi:10.1186/s13321-020-0411-2

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…We provide enriched functions so that users could filter materials for their required elements. The visualization was also supported by our previously developed software, named 3DStructGen 35 with many properties shown on the webpage. In parallel, the material database can cover a limited range of materials, and the relatively slow DFT computation requires a fast and accurate substitute for material design.…”

Section: ■ Discussion and Conclusionmentioning

confidence: 99%

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Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning

Chen

Yan

et al. 2022

J. Phys. Chem. C

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Predicting physical and chemical properties of materials based on structures is critical for bottom-up material design. Many property prediction models and material training databases have been proposed, but accurately predicting properties is still challenging. Here, we report a package of “Matgen + CrystalNet” approach to improve material property prediction. We construct a large-scale material genome database (Matgen) containing 76k materials collected from an experimentally observed database and compute their properties through the density functional theory method with the Perdew–Burke–Ernzerhof (PBE) functional. Our database achieves the same computation accuracy by comparing part of our results with those from the open Material Project and Open Quantum Materials Database, all with PBE computations, and contains more diverse chemical species and big-sized structures. Based on the computed properties of our comprehensive data set, we have developed a new graph neural network (GNN) model, namely, CrystalNet, by strengthening the message passing between atoms and bonds to mimic physical and chemical interactions. The model is shown to outperform other GNN prediction models. The proof-of-concept applications, such as fine-tuning data on experimental values to improve prediction accuracy and bandgap prediction on hypothetical materials, showcase the usability and potential capacity of our package of “database + model” to improve material design.

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Section: ■ Discussion and Conclusionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning

Chen

Yan

et al. 2022

J. Phys. Chem. C

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“…By leveraging ML techniques and highthroughput simulations, it enables the rapid exploration of chemical space and the design of molecules with the desired properties. The availability of user-friendly interfaces and its integration with existing computational chemistry software, such as VASP and Gaussian, further enhances its usability and applicability in various research domains [125].…”

Section: Ai Programs and Platforms Used For Drug Discoverymentioning

confidence: 99%

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Visan,

Negut

2024

Life

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Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI’s role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

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Probing on crystallographic structural and surface morphology of hydrothermally synthesized MoS2 nanoflowers consisting of nanosheets

Kumar¹,

Siroha²,

Sharma³

et al. 2021

Applied Surface Science Advances

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3DStructGen: an interactive web-based 3D structure generation for non-periodic molecule and crystal

Cited by 6 publications

References 24 publications

Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning

Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Probing on crystallographic structural and surface morphology of hydrothermally synthesized MoS2 nanoflowers consisting of nanosheets

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