Cybernetic methods of drug design. I. Statement of the problem—The perceptron approach

Hiller, Sebastian; Golender, V. E.; Rosenblit, A.B.; Rastrigin, Leonard Andreevich; Glaz, A. B.

doi:10.1016/0010-4809(73)90074-8

Cited by 30 publications

(14 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both generative models and analytical techniques have been extensively used in the qualitative/quantitative search of patterns underlying chemical systems (Elton et al, 2019;Ghosh et al, 2019;Stein et al, 2019a,b). It should be noted the use data from large repositories (e.g., Protein Data Bank and Cambridge Structural Database) and ML methods are not new (Hiller et al, 1973;Gasteiger and Zupan, 1993;Behler, 2016). The latter have been employed as classification tools in pioneering works, encompassing, for e.g., the analysis of spectra (Thomsen and Meyer, 1989), quantification of structure-activity relationships (QSARs) (Agrafiotis et al, 2002), and prediction of binding sites of biomolecules (Keil et al, 2004).…”

Section: Co-occurring Machine-learning Contributions In Chemical Sciementioning

confidence: 99%

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2019

View full text Add to dashboard Cite

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

show abstract

Section: Co-occurring Machine-learning Contributions In Chemical Sciementioning

confidence: 99%

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2019

View full text Add to dashboard Cite

show abstract

“…The non-quantitative SAR (Structure-Activity Relationships) models developed in the 1970s by Hiller, 37,38 Golender and Rosenblit, 39,40 Piruzyan, Avidon et al, 41 Cramer, 42 Brugger, Stuper and Jurs, 43,44 and Hodes et al 45 were inspired by the, at that time, popular artificial intelligence, expert systems, machine learning and pattern recognition paradigms. In those approaches, chemical structures were described by means of indicators of the presence of structural fragments interpreted as topological (or 2D) pharmacophores (biophores, toxophores, etc.)…”

Section: Historical Surveymentioning

confidence: 99%

Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening

Baskin¹,

Varnek²

2008

Chemoinformatics Approaches to Virtual Screening

View full text Add to dashboard Cite

“…The first application of ANNs to drug discovery dates back to the early 1970s, when Hiller et al 10 published a study using the Rosenblatt perceptron to classify substituted 1,3-dioxanes as physiologically active or inactive. In this work, elements of the chemical structures were projected onto the perceptron retina; the perceptron was trained using a set of compounds with known activities, and the trained neural network demonstrated good recognition ability on both the training and the test sets of compounds.…”

Section: Introductionmentioning

confidence: 99%

A renaissance of neural networks in drug discovery

Baskin

Winkler

Tetko

2016

Expert Opinion on Drug Discovery

213

149

View full text Add to dashboard Cite

Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

show abstract

Cybernetic methods of drug design. I. Statement of the problem—The perceptron approach

Cited by 30 publications

References 2 publications

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening

A renaissance of neural networks in drug discovery

Contact Info

Product

Resources

About