scholarly journals De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry

2021 ◽  
Author(s):  
Michal Pikusa ◽  
Olivier Rene ◽  
Sarah Williams ◽  
Yen-Liang Chen ◽  
Eric Martin ◽  
...  
Keyword(s):  
Chagas Disease ◽  
De Novo ◽  
Biological Activities ◽  
Generative Adversarial Network ◽  
De Novo Drug Design ◽  
Adversarial Network ◽  
Structure Activity ◽  
Recent Developments ◽  
Quantitative Structure Activity Relationships

Designing novel molecules with targeted biological activities and optimized physicochemical properties is a challenging endeavor in drug discovery. Recent developments in artificial intelligence have enhanced the early steps of de novo drug design and compound optimization. Herein, we present a generative adversarial network trained to design new chemical matter that satisfies a given biological signature. Our model, called pqsar2cpd, is based on the activity of compounds across multiple assays obtained via pQSAR (profile-quantitative structure-activity relationships). We applied pqsar2cpd to Chagas disease and designed a novel molecule that was experimentally confirmed to inhibit growth of parasites in vitro at low micromolar concentrations. Altogether, this approach bridges chemistry and biology into one single framework for the design of novel molecules with promising biological activity.

scholarly journals Recent Developments in Inonotus obliquus (Chaga mushroom) Polysaccharides: Isolation, Structural Characteristics, Biological Activities and Application

Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1441
Author(s):  
Yangpeng Lu ◽  
Yanan Jia ◽  
Zihan Xue ◽  
Nannan Li ◽  
Junyu Liu ◽  
...  
Keyword(s):  
Biological Activities ◽  
Structural Characteristics ◽  
Potential Candidate ◽  
Health Food ◽  
Inonotus Obliquus ◽  
Recent Developments ◽  
Inonotus Obliquus Polysaccharide ◽  
Future Work

Inonotus obliquus (Chaga mushroom) is a kind of medicine and health food widely used by folk in China, Russia, Korea, and some occidental countries. Among the extracts from Inonotus obliquus, Inonotus obliquus polysaccharide (IOPS) is supposed to be one of the major bioactive components in Inonotus obliquus, which possesses antitumor, antioxidant, anti-virus, hypoglycemic, and hypolipidemic activities. In this review, the current advancements on extraction, purification, structural characteristics, and biological activities of IOPS were summarized. This review can provide significant insight into the IOPS bioactivities as their in vitro and in vivo data were summarized, and some possible mechanisms were listed. Furthermore, applications of IOPS were reviewed and discussed; IOPS might be a potential candidate for the treatment of cancers and type 2 diabetes. Besides, new perspectives for the future work of IOPS were also proposed.

Design, synthesis and biological activities of pyrrole-3-carboxamide derivatives as EZH2 (enhancer of zeste homologue 2) inhibitors and anticancer agents

2020 ◽  
Vol 44 (6) ◽  
pp. 2247-2255
Author(s):  
Qifan Zhou ◽  
Lina Jia ◽  
Fangyu Du ◽  
Xiaoyu Dong ◽  
Wanyu Sun ◽  
...  
Keyword(s):  
Biological Activity ◽  
Anticancer Agents ◽  
Biological Activities ◽  
Activity Assay ◽  
Design Synthesis ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Enhancer Of Zeste

A novel series of pyrrole-3-carboxamides targeting EZH2 have been designed and synthesized. The structure–activity relationships were summarized by combining with in vitro biological activity assay and docking results.

scholarly journals Biological and Pharmacological Effects of Synthetic Saponins

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 4974
Author(s):  
Yu-Pu Juang ◽  
Pi-Hui Liang
Keyword(s):  
Biological Activities ◽  
Structural Diversity ◽  
Biological Evaluation ◽  
Steroid Saponin ◽  
Immunomodulatory Effects ◽  
Traditional Medicines ◽  
Amphiphilic Molecules ◽  
Structure Activity ◽  
Recent Developments ◽  
Structure Heterogeneity

Saponins are amphiphilic molecules consisting of carbohydrate and either triterpenoid or steroid aglycone moieties and are noted for their multiple biological activities—Fungicidal, antimicrobial, antiviral, anti-inflammatory, anticancer, antioxidant and immunomodulatory effects have all been observed. Saponins from natural sources have long been used in herbal and traditional medicines; however, the isolation of complexed saponins from nature is difficult and laborious, due to the scarce amount and structure heterogeneity. Chemical synthesis is considered a powerful tool to expand the structural diversity of saponin, leading to the discovery of promising compounds. This review focuses on recent developments in the structure optimization and biological evaluation of synthetic triterpenoid and steroid saponin derivatives. By summarizing the structure–activity relationship (SAR) results, we hope to provide the direction for future development of saponin-based bioactive compounds.

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Chemical Space ◽  
Learning Method ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Structure Generation ◽  
Adversarial Network ◽  
Molecule Design ◽  
Novel Structures

<p>Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new deep learning method, LatentGAN, which combine an autoencoder and a generative adversarial neural network for doing de novo molecule design. We applied the method for structure generation in two scenarios, one is to generate random drug-like compounds and the other is to generate target biased compounds. Our results show that the method works well in both cases, in which sampled compounds from the trained model can largely occupy the same chemical space of the training set and still a substantial fraction of the generated compound are novel. The distribution of drug-likeness score for compounds sampled from LatentGAN is also similar to that of the training set.</p>

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Esben Jannik Bjerrum ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
De Novo Molecular Design ◽  
Novel Structures

<p> </p><p>Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases: sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.</p><p> </p>

scholarly journals Combining Generative Artificial Intelligence and On-Chip Synthesis for De Novo Drug Design

2020 ◽  
Author(s):  
Francesca Grisoni ◽  
Berend Huisman ◽  
Alexander Button ◽  
Michael Moret ◽  
Kenneth Atz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Design ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Amide Bond ◽  
Reaction Products ◽  
De Novo Drug Design ◽  
On Chip

<p>Automation of the molecular design-make-test-analyze cycle speeds up the identification of hit and lead compounds for drug discovery. Using deep learning for computational molecular design and a customized microfluidics platform for on-chip compound synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space defined by known LXRα agonists, and to suggest structural analogs of known ligands and novel molecular cores. To further the design of lead-like molecules and ensure compatibility with automated on-chip synthesis, this chemical space was confined to the set of virtual products obtainable from 17 different one-step reactions. Overall, 25 <i>de novo</i> generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass <i>in vitro</i> activity screening of the crude reaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch re-synthesis, purification, and re-testing of 14 of these compounds confirmed that 12 of them were potent LXRα or LXRβ agonists. These results support the utilization of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.<b></b></p>

scholarly journals Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4795
Author(s):  
Ajaykumar Gandhi ◽  
Vijay Masand ◽  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Anis Ben Ghorbal ◽  
...  
Keyword(s):  
De Novo ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Structure Activity ◽  
Tnbc Cell ◽  
Qsar Models ◽  
Anti Tumor Activity

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

2018 ◽  
Author(s):  
Oscar Mendez-Lucio ◽  
Benoit Baillif ◽  
Djork-Arné Clevert ◽  
David Rouquié ◽  
Joerg Wichard
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Gene Expression Signature ◽  
Cellular Level ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Gene Expression Signatures

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular <i>de novo</i> design and compound optimization. Herein, we report the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

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