de novo design
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2022 ◽  
pp. 2102185
Author(s):  
Kangqiang Qiu ◽  
Ryo Seino ◽  
Guanqun Han ◽  
Munetaka Ishiyama ◽  
Yuichiro Ueno ◽  
...  
Keyword(s):  
De Novo ◽  

2022 ◽  
pp. 191-204
Author(s):  
Subha Sankar Paul ◽  
Debarun Dhali ◽  
Yazen Yaseen ◽  
Upasana Basu ◽  
Shilpa Pal ◽  
...  

2021 ◽  
Author(s):  
Masanori Nagao ◽  
Ai Yamaguchi ◽  
Teruhiko Matsubara ◽  
Yu Hoshino ◽  
Toshinori Sato ◽  
...  

2021 ◽  
Author(s):  
Maud Parrot ◽  
Hamza Tajmouati ◽  
Vinicius Barros Ribeiro da Silva ◽  
Brian Ross Atwood ◽  
Robin Fourcade ◽  
...  

Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule synthesizability, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). After a comparison of RScore with other synthetic scores from the literature, we describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables to obtain more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under-synthetic- constraint.


2021 ◽  
Author(s):  
Maud Parrot ◽  
Hamza Tajmouati ◽  
Vinicius Barros Ribeiro da Silva ◽  
Brian Ross Atwood ◽  
Robin Fourcade ◽  
...  

Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule synthesizability, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). After a comparison of RScore with other synthetic scores from the literature, we describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables to obtain more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under-synthetic- constraint.


2021 ◽  
Author(s):  
Tamuka M Chidyausiku ◽  
Soraia R Mendes ◽  
Jason C Klima ◽  
Ulrich Eckhard ◽  
Scott Houliston ◽  
...  

Antibodies and antibody derivatives such as nanobodies contain immunoglobulin-like (Ig) [beta]-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here we develop design rules for the central feature of the Ig fold architecture - the non-local cross-[beta]; structure connecting the two [beta]-sheets - and use these to de novo design highly stable seven-stranded Ig domains, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of a new class of antibody-like scaffolds with tailored structures and superior biophysical properties.


2021 ◽  
Author(s):  
Pieter H Bos ◽  
Evelyne M. Houang ◽  
Fabio Ranalli ◽  
Abba E. Leffler ◽  
Nicholas A. Boyles ◽  
...  

The lead optimization stage of a drug discovery program generally involves the design, synthesis and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm. AutoDesigner employs a cloud-native, multi-stage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of a SBDD project where limited data is available. To assess the effectiveness of AutoDesigner, we applied it to the design of novel inhibitors of D-amino acid oxidase (DAO), a target for the treatment of schizophrenia. AutoDesigner was able to generate and efficiently explore over 1 billion molecules to successfully address a variety of project goals. The compounds generated by AutoDesigner that were synthesized and assayed (1) simultaneously met not only physicochemical criteria, clearance and central nervous system (CNS) penetration (Kp,uu) cutoffs, but also potency thresholds; (2) fully utilize structural data to discover and explore novel interactions and a previously unexplored subpocket in the DAO active site. The reported data demonstrate that AutoDesigner can play a key role in accelerating the discovery of novel, potent chemical matter within the constraints of a given drug discovery lead optimization campaign.


2021 ◽  
Author(s):  
Chong Lu ◽  
Shien Liu ◽  
Weihua Shi ◽  
Jun Yu ◽  
Zhou Zhou ◽  
...  

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key of virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against PHGDH, proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.


2021 ◽  
pp. 113897
Author(s):  
Kun Liu ◽  
Yunsen Zhang ◽  
Ke Liu ◽  
Yunqiu Zhao ◽  
Bei Gao ◽  
...  

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1805
Author(s):  
Ana L. Chávez-Hernández ◽  
K. Eurídice Juárez-Mercado ◽  
Fernanda I. Saldívar-González ◽  
José L. Medina-Franco

Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.


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