Synthesizing a Neuron Using Chemical Reactions

Fang, Chongzhou; Shen, Ziyuan; Zhang, Zaichen; You, Xiaohu; Zhang, Chuan

doi:10.1109/sips.2018.8598458

Cited by 3 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, CRNs could be employed to not only analyse the existing chemical systems but also construct new chemical systems in a convenient way, without considering the physical substrate. Using CRNs, researchers have designed novel molecular devices that achieve functions originally in electronic field, e.g., combinational logic, sequential logic, discrete-time signal processing computations, and arithmetic elements [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. CRNs are always modeled in terms of mass-action kinetics [52,53], which are demonstrated by ordinary differential equations (ODEs) [54][55][56][57][58].…”

Section: Chemical Reaction Networkmentioning

confidence: 99%

DNA computing for combinational logic

Zhang

Zhuang

et al. 2018

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

With the progressive scale-down of semiconductor's feature size, people are looking forward to More Moore and More than Moore. In order to offer a possible alternative implementation process, people are trying to figure out a feasible transfer from silicon to molecular computing. Such transfer lies on bio-based modules programming with computer-like logic, aiming at realizing the Turing machine. To accomplish this, the DNA-based combinational logic is inevitably the first step we have taken care of. This timely overview paper introduces combinational logic synthesized in DNA computing from both analog and digital perspectives separately. State-of-the-art research progress is summarized for interested readers to quick understand DNA computing, initiate discussion on existing techniques and inspire innovation solutions. We hope this paper can pave the way for the future DNA computing synthesis.

show abstract

Section: Chemical Reaction Networkmentioning

confidence: 99%

DNA computing for combinational logic

Zhang

Zhuang

et al. 2018

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“… 7 − 9 Deep learning has been successfully applied in compound property prediction, 10 , 11 de novo design, 12 − 14 lead discovery, 15 repurposing, 16 − 18 and synthetic design. 19 Deep learning models demonstrate significant improvements in rapidly screening potent leads from massive compounds in available compound libraries. More recently, deep learning models achieved encouraging results in identifying antibacterial compounds, 18 candidates against osteoclastogenesis, 20 repurposing candidates for COVID-19.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, there has been a growing enthusiasm for using deep learning to advance drug discovery. − Deep learning has been successfully applied in compound property prediction, , de novo design, − lead discovery, repurposing, − and synthetic design . Deep learning models demonstrate significant improvements in rapidly screening potent leads from massive compounds in available compound libraries.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity

et al. 2022

View full text Add to dashboard Cite

Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently. Deep learning algorithms, in particular, have shown promise in terms of swiftly screening effective leads from huge compound libraries and producing novel compounds with desirable features. We used a deep neural network to search for potent β-microtubule inhibitors in natural goods. Eleutherobin, bruceine D (BD), and phorbol 12-myristate 13-acetate (PMA) are three highly effective natural compounds that have been found as β-microtubule inhibitors. In conclusion, this paper describes the use of deep learning to screen for effective β-microtubule inhibitors. This research also demonstrates the promising possibility of employing deep learning to develop drugs from natural products for a wider range of disorders.

show abstract

DNA computing for combinational logic

Zhang

Zhuang

et al. 2019

Sci. Sin.-Inf.

Self Cite

View full text Add to dashboard Cite

With the progressive scale-down of semiconductor feature size, people are looking forward to More Moore and More than Moore. Thus, people are trying to devise a feasible transfer from silicon to molecular computing to offer a possible alternative implementation process. Such a transfer is based on bio-based module programming with computer-like logic, aimed at realizing the Turing machine. DNA-based combinational logic is inevitably the first step that we have taken to accomplish this. This timely overview paper introduces combinational logic separately synthesized in DNA computing from both analog and digital perspectives. State-of-the-art research progress is summarized herein for interested readers to quickly understand DNA computing, initiate discussion on existing techniques, and inspire innovative solutions. We hope this review paves the way for future DNA computing synthesis.

show abstract

Synthesizing a Neuron Using Chemical Reactions

Cited by 3 publications

References 16 publications

DNA computing for combinational logic

DNA computing for combinational logic

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity

DNA computing for combinational logic

Contact Info

Product

Resources

About