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Caminero Gomes Soares A, Marques Sousa GH, Calil RL, Goulart Trossini GH. Absorption matters: A closer look at popular oral bioavailability rules for drug approvals. Mol Inform 2023; 42:e202300115. [PMID: 37550251 DOI: 10.1002/minf.202300115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/10/2023] [Accepted: 08/07/2023] [Indexed: 08/09/2023]
Abstract
This study examines how two popular drug-likeness concepts used in early development, Lipinski Rule of Five (Ro5) and Veber's Rules, possibly affected drug profiles of FDA approved drugs since 1997. Our findings suggest that when all criteria are applied, relevant compounds may be excluded, addressing the harmfulness of blindly employing these rules. Of all oral drugs in the period used for this analysis, around 66 % conform to the RO5 and 85 % to Veber's Rules. Molecular Weight and calculated LogP showed low consistent values over time, apart from being the two least followed rules, challenging their relevance. On the other hand, hydrogen bond related rules and the number of rotatable bonds are amongst the most followed criteria and show exceptional consistency over time. Furthermore, our analysis indicates that topological polar surface area and total count of hydrogen bonds cannot be used as interchangeable parameters, contrary to the original proposal. This research enhances the comprehension of drug profiles that were FDA approved in the post-Lipinski period. Medicinal chemists could utilize these heuristics as a limited guide to direct their exploration of the oral bioavailability chemical space, but they must also steer the wheel to break these rules and explore different regions when necessary.
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Affiliation(s)
- Artur Caminero Gomes Soares
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
| | - Gustavo Henrique Marques Sousa
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
| | - Raisa Ludmila Calil
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
| | - Gustavo Henrique Goulart Trossini
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
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Networks for better drug discovery: making optimal use of existing chemical space. Future Med Chem 2018; 10:2497-2499. [PMID: 30517037 DOI: 10.4155/fmc-2018-0265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. Mol Pharm 2017; 14:3098-3104. [PMID: 28703000 DOI: 10.1021/acs.molpharmaceut.7b00346] [Citation(s) in RCA: 234] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.
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Affiliation(s)
- Artur Kadurin
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States.,Steklov Mathematical Institute at St. Petersburg , St. Petersburg 191023, Russia.,Kazan Federal University , Kazan, Republic of Tatarstan 420008, Russia
| | - Sergey Nikolenko
- National Research University Higher School of Economics , St. Petersburg 190008, Russia.,Steklov Mathematical Institute at St. Petersburg , St. Petersburg 191023, Russia.,Kazan Federal University , Kazan, Republic of Tatarstan 420008, Russia
| | - Kuzma Khrabrov
- Search Department, Mail.Ru Group Ltd. , Moscow 125167, Russia
| | - Alex Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States
| | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern , Baltimore, Maryland 21218, United States.,The Biogerontology Research Foundation , Trevissome Park, Truro TR4 8UN, U.K.,Moscow Institute of Physics and Technology , Dolgoprudny 141701, Russia
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Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 2017; 8:10883-10890. [PMID: 28029644 PMCID: PMC5355231 DOI: 10.18632/oncotarget.14073] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 11/24/2016] [Indexed: 12/19/2022] Open
Abstract
Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.
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Affiliation(s)
- Artur Kadurin
- Search Department, Mail.Ru Group Ltd., Moscow, Russia.,Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Big Data and Text Analysis Laboratory, Kazan Federal University, Kazan, Republic of Tatarstan, Russia.,St. Petersburg Department of V.A. Steklov Institute of Mathematics of the Russian Academy of Sciences, Petersburg, Russia
| | - Alexander Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Andrey Kazennov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Polina Mamoshina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Department of Computer Science, University of Oxford, Oxford, UK
| | - Quentin Vanhaelen
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | | | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,The Biogerontology Research Foundation, Trevissome Park, Truro TR4 8UN, UK.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
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Pairas GN, Perperopoulou F, Tsoungas PG, Varvounis G. The Isoxazole Ring and ItsN-Oxide: A Privileged Core Structure in Neuropsychiatric Therapeutics. ChemMedChem 2017; 12:408-419. [DOI: 10.1002/cmdc.201700023] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 02/13/2017] [Indexed: 01/23/2023]
Affiliation(s)
- George N. Pairas
- Laboratory of Medicinal Chemistry, Department of Pharmacy; University of Patras; 265 04 Patras Greece
| | - Fereniki Perperopoulou
- Laboratory of Enzyme Technology, Department of Biotechnology; Agricultural University of Athens; 75 Iera Odos St. 118 55 Athens Greece
| | - Petros G. Tsoungas
- Laboratory of Biochemistry; Hellenic Pasteur Institute; 127 Vas. Sofias Ave. 115 21 Athens Greece
| | - George Varvounis
- Section of Organic Chemistry and Biochemistry, Department of Chemistry; University of Ioannina; 451 10 Ioannina Greece
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Osolodkin DI, Radchenko EV, Orlov AA, Voronkov AE, Palyulin VA, Zefirov NS. Progress in visual representations of chemical space. Expert Opin Drug Discov 2015; 10:959-73. [DOI: 10.1517/17460441.2015.1060216] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Awale M, Reymond JL. Atom Pair 2D-Fingerprints Perceive 3D-Molecular Shape and Pharmacophores for Very Fast Virtual Screening of ZINC and GDB-17. J Chem Inf Model 2014; 54:1892-907. [DOI: 10.1021/ci500232g] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Mahendra Awale
- Department of Chemistry and
Biochemistry, University of Berne, Freiestrasse 3, 3012 Berne Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and
Biochemistry, University of Berne, Freiestrasse 3, 3012 Berne Switzerland
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Ruddigkeit L, Awale M, Reymond JL. Expanding the fragrance chemical space for virtual screening. J Cheminform 2014; 6:27. [PMID: 24876890 PMCID: PMC4037718 DOI: 10.1186/1758-2946-6-27] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Accepted: 05/12/2014] [Indexed: 12/30/2022] Open
Abstract
The properties of fragrance molecules in the public databases SuperScent and Flavornet were analyzed to define a “fragrance-like” (FL) property range (Heavy Atom Count ≤ 21, only C, H, O, S, (O + S) ≤ 3, Hydrogen Bond Donor ≤ 1) and the corresponding chemical space including FL molecules from PubChem (NIH repository of molecules), ChEMBL (bioactive molecules), ZINC (drug-like molecules), and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). The FL subsets of these databases were classified by MQN (Molecular Quantum Numbers, a set of 42 integer value descriptors of molecular structure) and formatted for fast MQN-similarity searching and interactive exploration of color-coded principal component maps in form of the FL-mapplet and FL-browser applications freely available at http://www.gdb.unibe.ch. MQN-similarity is shown to efficiently recover 15 different fragrance molecule families from the different FL subsets, demonstrating the relevance of the MQN-based tool to explore the fragrance chemical space.
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Affiliation(s)
- Lars Ruddigkeit
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
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Hoksza D, Skoda P, Voršilák M, Svozil D. Molpher: a software framework for systematic chemical space exploration. J Cheminform 2014; 6:7. [PMID: 24655571 PMCID: PMC3998053 DOI: 10.1186/1758-2946-6-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Accepted: 03/17/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology. RESULTS In this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space. Through a process we term 'molecular morphing', Molpher produces a path of structurally-related compounds. This path is generated by the iterative application of so-called 'morphing operators' that represent simple structural changes, such as the addition or removal of an atom or a bond. Molpher incorporates an optimized parallel exploration algorithm, compound logging and a two-dimensional visualization of the exploration process. Its feature set can be easily extended by implementing additional morphing operators, chemical fingerprints, similarity measures and visualization methods. Molpher not only offers an intuitive graphical user interface, but also can be run in batch mode. This enables users to easily incorporate molecular morphing into their existing drug discovery pipelines. CONCLUSIONS Molpher is an open-source software framework for the design of virtual chemical libraries focused on a particular mechanistic class of compounds. These libraries, represented by a morphing path and its surroundings, provide valuable starting data for future in silico and in vitro experiments. Molpher is highly extensible and can be easily incorporated into any existing computational drug design pipeline.
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Affiliation(s)
- David Hoksza
- Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic.
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