Bioisosteres of the Phenyl Ring: Recent Strategic Applications in Lead Optimization and Drug Design

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

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MolOpt: A Web Server for Drug Design using Bioisosteric Transformation

Current Computer - Aided Drug Design ◽

10.2174/1573409915666190704093400 ◽

2020 ◽

Vol 16 (4) ◽

pp. 460-466 ◽

Cited By ~ 1

Author(s):

Jinwen Shan ◽

Changge Ji

Keyword(s):

Drug Design ◽

Web Server ◽

Lead Optimization ◽

Similarity Comparison ◽

Transformation Rules ◽

In Silico Drug Design ◽

Bioisosteric Replacement ◽

Similar Chemical ◽

Transformation Potential ◽

Chemical Groups

Background: Bioisosteric replacement is widely used in drug design for lead optimization. However, the identification of a suitable bioisosteric group is not an easy task. Methods: In this work, we present MolOpt, a web server for in silico drug design using bioisosteric transformation. Potential bioisosteric transformation rules were derived from data mining, deep generative machine learning and similarity comparison. MolOpt tries to assist the medicinal chemist in his/her search for what to make next. Results and Discussion: By replacing molecular substructures with similar chemical groups, MolOpt automatically generates lists of analogues. MolOpt also evaluates forty important pharmacokinetic and toxic properties for each newly designed molecule. The transformed analogues can be assessed for possible future study. Conclusion: MolOpt is useful for the identification of suitable lead optimization ideas. The MolOpt Server is freely available for use on the web at http://xundrug.cn/molopt.

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AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

Journal of Cheminformatics ◽

10.1186/s13321-020-00429-4 ◽

2020 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Jacob O. Spiegel ◽

Jacob D. Durrant

Keyword(s):

Genetic Algorithm ◽

Drug Design ◽

Open Source ◽

De Novo ◽

Lead Optimization ◽

De Novo Drug Design

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Structure-based drug design of novel ASK1 inhibitors using an integrated lead optimization strategy

Bioorganic & Medicinal Chemistry Letters ◽

10.1016/j.bmcl.2017.02.079 ◽

2017 ◽

Vol 27 (8) ◽

pp. 1709-1713 ◽

Cited By ~ 11

Author(s):

Tony S. Gibson ◽

Benjamin Johnson ◽

Andrea Fanjul ◽

Petro Halkowycz ◽

Douglas R. Dougan ◽

...

Keyword(s):

Drug Design ◽

Lead Optimization ◽

Optimization Strategy ◽

Structure Based Drug Design

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Rapid and Accurate Estimation of Protein-Ligand Relative Binding Affinities using Site-Identification by Ligand Competitive Saturation

Chemical Science ◽

10.1039/d1sc01781k ◽

2021 ◽

Author(s):

Himanshu Goel ◽

Anthony Hazel ◽

Vincent D. Ustach ◽

Sunhwan Jo ◽

Wenbo Yu ◽

...

Keyword(s):

Drug Design ◽

Ligand Binding ◽

Lead Optimization ◽

Accurate Estimation ◽

Binding Affinities ◽

Structure Based Drug Design ◽

Relative Binding ◽

Central Pillar ◽

Site Identification

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The Site Identification by Ligand Competitive Saturation (SILCS) methodology is based on functional...

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SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

10.26434/chemrxiv.12271508 ◽

2020 ◽

Author(s):

Yuyao Yang ◽

Shuangjia Zheng ◽

Shimin Su ◽

Jun Xu ◽

Hongming Chen

Keyword(s):

Neural Networks ◽

Drug Discovery ◽

Drug Design ◽

State Of The Art ◽

The State ◽

Generative Model ◽

Lead Optimization ◽

Scaffold Hopping ◽

Reference Models ◽

Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (SyntaLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our SyntaLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that SyntaLinkercan be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

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AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

10.26434/chemrxiv.12271508.v3 ◽

2020 ◽

Author(s):

Yuyao Yang ◽

Shuangjia Zheng ◽

Shimin Su ◽

Jun Xu ◽

Hongming Chen

Keyword(s):

Neural Networks ◽

Drug Discovery ◽

Drug Design ◽

State Of The Art ◽

The State ◽

Generative Model ◽

Lead Optimization ◽

Scaffold Hopping ◽

Reference Models ◽

Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

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