Machine learning the ropes: principles, applications and directions in synthetic chemistry

2020 ◽  
Vol 49 (17) ◽  
pp. 6154-6168 ◽  
Author(s):  
Felix Strieth-Kalthoff ◽  
Frederik Sandfort ◽  
Marwin H. S. Segler ◽  
Frank Glorius
Keyword(s):  
Machine Learning ◽  
Molecular Design ◽  
Easy Access ◽  
Synthetic Chemistry ◽  
Design Synthesis ◽  
Synthesis Planning

Chemists go ML! This tutorial review provides easy access to the fundamentals of machine learning from a synthetic chemist's perspective. Its diverse applications for molecular design, synthesis planning, or reactivity prediction are summarized.

Restriction of Access to Dark State: A New Mechanistic Model for Heteroatom-Containing AIE Systems

2019 ◽  
Author(s):  
Yujie Tu ◽  
Junkai Liu ◽  
Haoke Zhang ◽  
Qian Peng ◽  
Jacky W. Y. Lam ◽  
...  
Keyword(s):  
Excited States ◽  
Radiative Decay ◽  
Molecular Design ◽  
Mechanistic Model ◽  
Zinc Ion ◽  
Easy Access ◽  
Triplet States ◽  
Dark State ◽  
Ion Probe ◽  
Π State

Aggregation-induced emission (AIE) is an unusual photophysical phenomenon and provides an effective and advantageous strategy for the design of highly emissive materials in versatile applications such as sensing, imaging, and theragnosis. "Restriction of intramolecular motion" is the well-recognized working mechanism of AIE and have guided the molecular design of most AIE materials. However, it sometimes fails to be workable to some heteroatom-containing systems. Herein, in this work, we take more than one excited state into account and specify a mechanism –"restriction of access to dark state (RADS)" – to explain the AIE effect of heteroatom-containing molecules. An anthracene-based zinc ion probe named APA is chosen as the model compound, whose weak fluorescence in solution is ascribed to the easy access from the bright (π,π*) state to the closelying dark (n,π*) state caused by the strong vibronic coupling of the two excited states. By either metal complexation or aggregation, the dark state is less accessible due to the restriction of the molecular motion leading to the dark state and elevation of the dark state energy, thus the emission of the bright state is restored. RADS is found to be powerful in elucidating the photophysics of AIE materials with excited states which favor non-radiative decay, including overlap-forbidden states such as (n,π*) and CT states, spin-forbidden triplet states, which commonly exist in heteroatom-containing molecules.

Development of Novel Glitazones as Antidiabetic Agents: Molecular Design, Synthesis, Evaluation of Glucose Uptake Activity and SAR Studies

2020 ◽  
Vol 17 (7) ◽  
pp. 840-849
Author(s):  
Mahendra Gowdru Srinivas ◽  
Prabitha Prabhakaran ◽  
Subhankar Probhat Mandal ◽  
Yuvaraj Sivamani ◽  
Pranesh Guddur ◽  
...  
Keyword(s):  
Glucose Uptake ◽  
Molecular Design ◽  
Docking Studies ◽  
Antidiabetic Activity ◽  
Design Synthesis ◽  
In Silico Docking ◽  
Ppar Γ ◽  
Sar Studies ◽  
Structure Activity ◽  
Uptake Activity

Background: Thiazolidinediones and its bioisostere, namely, rhodanines have become ubiquitous class of heterocyclic compounds in drug design and discovery. In the present study, as part of molecular design, a series of novel glitazones that are feasible to synthesize in our laboratory were subjected to docking studies against PPAR-γ receptor for their selection. Methods and Results: As part of the synthesis of selected twelve glitazones, the core moiety, pyridine incorporated rhodanine was synthesized via dithiocarbamate. Later, a series of glitazones were prepared via Knovenageal condensation. In silico docking studies were performed against PPARγ protein (2PRG). The titled compounds were investigated for their cytotoxic activity against 3T3-L1 cells to identify the cytotoxicity window of the glitazones. Further, within the cytotoxicity window, glitazones were screened for glucose uptake activity against L6 cells to assess their possible antidiabetic activity. Conclusion: Based on the glucose uptake results, structure activity relationships are drawn for the title compounds.

scholarly journals Synthesis and Theoretical Investigation Using DFT of 2,3-Diphenylquinoxaline Derivatives for Electronic and Photovoltaic Effects

2021 ◽  
Author(s):  
Dorota Zając ◽  
Dariusz Przybylski ◽  
Jadwiga Sołoducho
Keyword(s):  
Solar Cells ◽  
Acrylic Acid ◽  
Organic Semiconductors ◽  
Organic Solar Cells ◽  
Molecular Design ◽  
Low Cost ◽  
Design Synthesis ◽  
Semiconducting Properties ◽  
Homo Lumo

AbstractDeveloping effective and low‐cost organic semiconductors is an opportunity for the development of organic solar cells (OPV). Herein, we report the molecular design, synthesis and characterization of two molecules with D–A–D–A configuration: 2-cyano-3-(5-(8-(3,4-ethylenodioxythiophen-5-yl)-2,3-diphenylquinoxalin-5-yl)thiophen-2-yl)acrylic acid (6) and 2-cyano-3-(5-(2,3-diphenyl-8-(thiophen-2-yl)quinoxalin-5-yl)thiophen-2-yl)acrylic acid (7). Moreover, we investigated the structural, theoretical and optical properties. The distribution of HOMO/LUMO orbitals and the values of the ionization potential indicate good semiconducting properties of the compounds and that they can be a bipolar material. Also, the optical study show good absorption in visible light (λabs 380–550 nm). We investigate the theoretical optoelectronic properties of obtained compounds as potential materials for solar cells.

Molecular design, synthesis and biological evaluation of 1,4-dihydro-4-oxoquinoline ribonucleosides as TcGAPDH inhibitors with trypanocidal activity

2013 ◽  
Vol 23 (16) ◽  
pp. 4597-4601 ◽  
Author(s):  
Fabyana A. Soares ◽  
Renata Sesti-Costa ◽  
João Santana da Silva ◽  
Maria Cecília B.V. de Souza ◽  
Vitor F. Ferreira ◽  
...  
Keyword(s):  
Molecular Design ◽  
Biological Evaluation ◽  
Design Synthesis ◽  
Trypanocidal Activity ◽  
Synthesis And Biological Evaluation

Naphthalimide–thiazoles as novel photonucleases: molecular design, synthesis, and evaluation

2004 ◽  
Vol 45 (6) ◽  
pp. 1247-1251 ◽  
Author(s):  
Yonggang Li ◽  
Yufang Xu ◽  
Xuhong Qian ◽  
Baoyuan Qu
Keyword(s):  
Molecular Design ◽  
Design Synthesis

scholarly journals Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design

2021 ◽  
Author(s):  
Alain Beaudelaire Tchagang ◽  
Ahmed H. Tewfik ◽  
Julio J. Valdés
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
High Speed ◽  
Density Functional ◽  
Molecular Design ◽  
Absolute Error ◽  
Molecular Data ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Short Time

Abstract Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

scholarly journals Use of Augmented Reality to Create an iOS App with Watson Studio

2021 ◽  
Vol 4 ◽  
pp. 98-100
Author(s):  
Semen Gorokhovskyi ◽  
Yelyzaveta Pyrohova
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Augmented Reality ◽  
New Technologies ◽  
Higher Education Institutions ◽  
Rapid Development ◽  
Easy Access ◽  
Smart Devices ◽  
The Real ◽  
The World

With the rapid development of applications for mobile platforms, developers from around the world already understand the need to impress with new technologies and the creation of such applications, with which the consumer will plunge into the world of virtual or augmented reality. Some of the world’s most popular mobile operating systems, Android and iOS, already have some well-known tools to make it easier to work with the machine learning industry and augmented reality technology. However, it cannot be said that their use has already reached its peak, as these technologies are at the stage of active study and development. Every year the demand for mobile application developers increases, and therefore more questions arise as to how and from which side it is better to approach immersion in augmented reality and machine learning. From a tourist point of view, there are already many applications that, with the help of these technologies, will provide more information simply by pointing the camera at a specific object.Augmented Reality (AR) is a technology that allows you to see the real environment right in front of us with a digital complement superimposed on it. Thanks to Ivan Sutherland’s first display, created in 1968 under the name «Sword of Damocles», paved the way for the development of AR, which is still used today.Augmented reality can be divided into two forms: based on location and based on vision. Location-based reality provides a digital picture to the user when moving through a physical area thanks to a GPS-enabled device. With a story or information, you can learn more details about a particular location. If you use AR based on vision, certain user actions will only be performed when the camera is aimed at the target object.Thanks to advances in technology that are happening every day, easy access to smart devices can be seen as the main engine of AR technology. As the smartphone market continues to grow, consumers have the opportunity to use their devices to interact with all types of digital information. The experience of using a smartphone to combine the real and digital world is becoming more common. The success of AR applications in the last decade has been due to the proliferation and use of smartphones that have the capabilities needed to work with the application itself. If companies want to remain competitive in their field, it is advisable to consider work that will be related to AR.However, analyzing the market, one can see that there are no such applications for future entrants to higher education institutions. This means that anyone can bring a camera to the university building and learn important information. The UniApp application based on the existing Swift and Watson Studio technologies was developed to simplify obtaining information on higher education institutions.

scholarly journals V-dock: fast generation of novel drug-like molecules using machine-learning-based docking score and molecular optimization

2021 ◽  
Author(s):  
Jieun Choi ◽  
Juyong Lee
Keyword(s):  
Machine Learning ◽  
Molecular Design ◽  
Biological Data ◽  
Ligand Docking ◽  
Molecular Property ◽  
Optimization Approach ◽  
Generation Process ◽  
Activity Data ◽  
Docking Score ◽  
Novel Drug

In this work, we propose a novel drug-like molecular design workflow by combining an efficient global molecular property optimization, protein-ligand molecular docking, and machine learning. Computational drug design algorithms aim to find novel molecules satisfying various drug-like properties and have a strong binding affinity between a protein and a ligand. To accomplish this goal, various computational molecular generation methods have been developed with recent advances in deep learning and the increase of biological data. However, most existing methods heavily depend on experimental activity data, which are not available for many targets. Thus, when the number of available activity data is limited, protein-ligand docking calculations should be used. However, performing a docking calculation during molecular generation on the fly requires considerable computational resources. To address this problem, we used machine-learning models predicting docking energy to accelerate the molecular generation process. We combined this ML-assisted docking score prediction model with the efficient global molecular property optimization approach, MolFinder. We call this design approach V-dock. Using the V-dock approach, we quickly generated many molecules with high docking scores for a target protein and desirable drug-like and bespoke properties, such as similarity to a reference molecule.

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