scholarly journals Organic chemistry synthesis problem as artificial intelligence planning.

2021 ◽  
Author(s):  
Arman Masoumi
Keyword(s):  
Organic Chemistry ◽  
Artificial Intelligence ◽  
Functional Groups ◽  
Organic Synthesis ◽  
Chemical Reactions ◽  
Benchmark Problems ◽  
Logical Language ◽  
Artificial Intelligence Planning ◽  
Planning Problems ◽  
Novel Algorithm

This thesis formulates organic chemistry synthesis problems as Artificial Intelligence planning problems and uses a combination of techniques developed in the field of planning to solve organic synthesis problems. To this end, a methodology for axiomatizing organic chemistry is developed, which includes axiomatizing molecules and functional groups, as well as two approaches for representing chemical reactions in a logical language amenable to reasoning. A novel algorithm for planning specific to organic chemistry is further developed, based on which a planner capable of identifying 75 functional groups and chemical classes is implemented with a knowledge base of 55 generic chemical reactions. The performance of the planner is empirically evaluated on two sets of benchmark problems and analytically compared with a number of competing algorithms. v

scholarly journals Organic chemistry synthesis problem as artificial intelligence planning.

2021 ◽  
Author(s):  
Arman Masoumi
Keyword(s):  
Organic Chemistry ◽  
Artificial Intelligence ◽  
Functional Groups ◽  
Organic Synthesis ◽  
Chemical Reactions ◽  
Benchmark Problems ◽  
Logical Language ◽  
Artificial Intelligence Planning ◽  
Planning Problems ◽  
Novel Algorithm

This thesis formulates organic chemistry synthesis problems as Artificial Intelligence planning problems and uses a combination of techniques developed in the field of planning to solve organic synthesis problems. To this end, a methodology for axiomatizing organic chemistry is developed, which includes axiomatizing molecules and functional groups, as well as two approaches for representing chemical reactions in a logical language amenable to reasoning. A novel algorithm for planning specific to organic chemistry is further developed, based on which a planner capable of identifying 75 functional groups and chemical classes is implemented with a knowledge base of 55 generic chemical reactions. The performance of the planner is empirically evaluated on two sets of benchmark problems and analytically compared with a number of competing algorithms. v

scholarly journals Modeling Organic Chemistry and Planning Organic Synthesis

10.29007/493z ◽  
2018 ◽  
Author(s):  
Arman Masoumi ◽  
Megan Antoniazzi ◽  
Mikhail Soutchanski
Keyword(s):  
Organic Chemistry ◽  
Organic Synthesis ◽  
Chemical Reactions ◽  
Practical Problem ◽  
State Of The Art ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Input Language ◽  
Domain Definition ◽  
Domain Independent

Organic Synthesis is a computationally challenging practical problem concerned with constructing a target molecule from a set of initially available molecules via chemical reactions. This paper demonstrates how organic synthesis can be formulated as a planning problem in Artificial Intelligence, and how it can be explored using the state-of-the-art domain independent planners.To this end, we develop a methodology to represent chemical molecules and generic reactions in PDDL 2.2, a version of the standardized Planning Domain Definition Language popular in AI. In our model, derived predicates define common functional groups and chemical classes in chemistry, and actionscorrespond to generic chemical reactions. We develop a set of benchmark problems. Since PDDL is supported as an input language by many modern planners, our benchmark can be subsequently useful forempirical assessment of the performance of various state-of-the-art planners.

Pipesworld: Applying Planning Systems to Pipeline Transportation

2004 ◽  
Author(s):  
Ruy L. Milidiu´ ◽  
Frederico dos Santos Liporace
Keyword(s):  
Artificial Intelligence ◽  
Transportation Problem ◽  
General Purpose ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Due Dates ◽  
Planning Systems ◽  
Pipeline Transportation ◽  
Specific Knowledge ◽  
Artificial Intelligence Planning

Most transportation problems consist of moving carriers of stationary cargo. Pipelines are unique in the sense that they are stationary carriers of moving cargo. As a consequence, the planning problem of these systems has singularities that make it very challenging. In this paper we present the Pipesworld model, a transportation problem inspired by the transportation of petroleum derivatives in Petrobras’ pipelines. Pipesworld takes into account important features like product interface constraints, limited product storage capacities and due dates for product delivery. The relevance and unique characteristics of Pipesworld has been recognized by the Artificial Intelligence planning community. Pipesworld has been selected as one of the benchmark problems to be used in the Fourth International Planning Competition, a biannual event to benchmark the state-of-the-art general purpose artificial planning systems. We report the results obtained by general purpose artificial intelligence planning systems when applied to the Pipesworld instances. We also analyze how different modelling techniques may be used to significantly improve the planners’ performance. Although the basic algorithms of these planners do not incorporate any specific knowledge about the pipeline transportation problem, the results obtained so far are quite satisfactory. We also describe our current work in developing Plumber, a dedicated solver, aimed to tackle effective operational situations. Plumber uses general purpose planning techniques but also incorporates domain specific knowledge and may work together with a human expert during the planning process. By applying Plumber to the Pipesworld instances, we compare its performance against general purpose planning systems. Preliminary tests with a first version of Plumber shows that it already outperforms Fast-Forward (FF), one of the best available general purpose planning systems. This shows that improved versions of Plumber have the potential to effectively deal with pipeline transportation operational scenarios.

scholarly journals Compact Tree Encodings for Planning as QBF

2018 ◽  
Vol 21 (62) ◽  
pp. 103-113
Author(s):  
Olivier Gasquet ◽  
Dominique Longin ◽  
Fr´ed´eric Maris ◽  
Pierre R´egnier ◽  
Ma¨el Valais
Keyword(s):  
Artificial Intelligence ◽  
Execution Time ◽  
Boolean Formula ◽  
Benchmark Problems ◽  
Sat Solvers ◽  
Quantified Boolean Formula ◽  
And Performance ◽  
Near Future ◽  
Planning Problems ◽  
Better Than

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

scholarly journals Compact Tree Encodings for Planning as QBF

2018 ◽  
Vol 21 (62) ◽  
pp. 103 ◽  
Author(s):  
Olivier Gasquet
Keyword(s):  
Artificial Intelligence ◽  
Execution Time ◽  
Boolean Formula ◽  
Benchmark Problems ◽  
Sat Solvers ◽  
Quantified Boolean Formula ◽  
And Performance ◽  
Near Future ◽  
Planning Problems ◽  
Better Than

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

scholarly journals Class Map: Improving Students’ Skills of Organic Synthesis in Learning Organic Chemistry for Pre-University Students

2022 ◽  
Vol 7 (1) ◽  
pp. 270-284
Author(s):  
Nik Mawar Hanifah Nik Hassan ◽  
Othman Talib ◽  
Hairul Faiezi Lokman
Keyword(s):  
Organic Chemistry ◽  
University Students ◽  
Organic Synthesis ◽  
Chemical Reactions ◽  
Functional Group ◽  
Document Analysis ◽  
Test Results ◽  
Chemistry Students ◽  
The Subject ◽  
Answering Questions

This action research uses the Kemmis & Mc Taggart Model (1988) to improve the skills for science stream of pre-university students in organic synthesis topic to convert one functional group to another by using Class Map in learning Organic Chemistry. The objectives of this study were to improve memory skills in conversion of functional groups in an Organic Chemistry reaction and to cultivate students' interest in the subject of Organic Chemistry. A total of six students of 6 Delta 2, SMK Sultan Abu Bakar were involved in this study. Preliminary surveys were conducted through observations, document analysis and interviews. The results of the survey showed that students could not remember the conversion of functional group well because in the Semester Three chemistry syllabus, there are too many chemical reactions, causing students less interested in learning Organic Chemistry. Students were exposed to the Class Map within two months. The test results displayed that (i) students can recall the functional group conversion reaction in an Organic Chemistry and (ii) students can apply the organic reactions learned in answering questions. The findings of the interviews showed that students can cultivate an interest in Organic Chemistry subject.

Recent Advances in the Applications of Triethylsilane in Organic Synthesis

Synthesis ◽  
2018 ◽  
Vol 50 (19) ◽  
pp. 3809-3824 ◽  
Author(s):  
Miroslav Soural ◽  
Veronika Ručilová
Keyword(s):  
Organic Chemistry ◽  
Functional Groups ◽  
Organic Synthesis ◽  
Reductive Cyclization ◽  
Reductive Coupling ◽  
Important Goal ◽  
Multiple Bonds ◽  
Recent Advances ◽  
3D Architecture

The synthesis of pharmacologically relevant scaffolds is an important goal in modern organic chemistry. For this reason, the use of methodologies involving operationally simple procedures and easily handled reagents to chemoselectively and stereoselectively convert different functionalities has gained considerable attention. In this review, we summarize the latest trends in reductive reactions using triethyl­silane as the key reagent that provide synthetically interesting intermediates, coupling products and structures with control of the 3D architecture.1 Introduction2 Scenario A: Reduction of C–C Multiple Bonds3 Scenario B: Reduction of Functional Groups4 Scenario C: Reductive Coupling5 Scenario D: Reductive Cyclization6 Conclusion

The molecular diversity of 1,8-diaminonaphthalene in organic chemistry

2020 ◽  
Vol 23 ◽  
Author(s):  
Ghodsi Mohammadi Ziarani ◽  
Fatemeh Mohajer ◽  
Suraj N. Mali
Keyword(s):  
Organic Chemistry ◽  
Organic Synthesis ◽  
Molecular Diversity ◽  
Organic Reactions ◽  
Recent Application ◽  
Organic Structure

: 1,8-diaminonaphthalene (1,8-DAN) with special organic structure was applied in organic synthesis to provide efficient complex scaffolds, through the two or four-component fashion. This review highlights its recent application in organic reactions under different conditions and heterogynous catalysts to produce various molecules, which were used as medicines, sensors, and dyes.

Modern Organic Synthesis in the Laboratory

2008 ◽  
Author(s):  
Jie Jack Li ◽  
Chris Limberakis ◽  
Derek A. Pflum
Keyword(s):  
Organic Chemistry ◽  
Graduate Students ◽  
Organic Synthesis ◽  
Functional Group ◽  
Bond Formation ◽  
Reaction Conditions ◽  
Carbon Bond ◽  
Oxidation Reduction ◽  
Carbon Carbon Bond ◽  
Daunting Task

Searching for reaction in organic synthesis has been made much easier in the current age of computer databases. However, the dilemma now is which procedure one selects among the ocean of choices. Especially for novices in the laboratory, it becomes a daunting task to decide what reaction conditions to experiment with first in order to have the best chance of success. This collection intends to serve as an "older and wiser lab-mate" one could have by compiling many of the most commonly used experimental procedures in organic synthesis. With chapters that cover such topics as functional group manipulations, oxidation, reduction, and carbon-carbon bond formation, Modern Organic Synthesis in the Laboratory will be useful for both graduate students and professors in organic chemistry and medicinal chemists in the pharmaceutical and agrochemical industries.

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