1
|
Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
Collapse
Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
| |
Collapse
|
2
|
Matos GDR, Pak S, Rizzo RC. Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. J Chem Inf Model 2023; 63:5803-5822. [PMID: 37698425 PMCID: PMC10694857 DOI: 10.1021/acs.jcim.3c01031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.
Collapse
Affiliation(s)
- Guilherme Duarte Ramos Matos
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Instituto de Química, Universidade de Brasília, Brasília, Distrito Federal, 70910-900, Brazil
| | - Steven Pak
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, New York, 11794, USA
| | - Robert C. Rizzo
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Institute of Chemical Biology & Drug Discovery, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| |
Collapse
|
3
|
Warr WA, Nicklaus MC, Nicolaou CA, Rarey M. Exploration of Ultralarge Compound Collections for Drug Discovery. J Chem Inf Model 2022; 62:2021-2034. [PMID: 35421301 DOI: 10.1021/acs.jcim.2c00224] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.
Collapse
Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Crewe, Cheshire CW4 7HZ, United Kingdom
| | - Marc C Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Christos A Nicolaou
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Matthias Rarey
- Universität Hamburg, ZBH Center for Bioinformatics, 20146 Hamburg, Germany
| |
Collapse
|
4
|
Abstract
Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .
Collapse
Affiliation(s)
- Francesca Grisoni
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
5
|
Arya H, Coumar MS. Design of novel ROCK inhibitors using fragment-based de novo drug design approach. J Mol Model 2020; 26:249. [PMID: 32829478 DOI: 10.1007/s00894-020-04493-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/30/2020] [Indexed: 12/01/2022]
Abstract
Rho-associated coiled-coil protein kinase (ROCK) is playing a vital role in the regulation of key cellular events and also responsible for causing several pathological conditions such as cancer, hypertension, Alzheimer's, cerebral vasospasm, and cardiac stroke. Therefore, it has attracted us to target ROCK protein as a potential therapeutic target for combating various diseases. Consequently, we investigated the active site of ROCK I protein and designed novel leads against the target using the de novo evolution drug design approach. Caffeic acid (an aglycone of acteoside) as a scaffold and fragments from 336 reported ROCK inhibitors were used for the design of novel leads. Multiple copy simultaneous search docking was used to identify the suitable fragments to be linked with the scaffold. Basic medicinal chemistry rules, coupled with structural insights generated by docking, led to the design of 7a, 8a, 9a, and 10a as potential ROCK I inhibitors. The designed leads showed better binding than the approved drug fasudil and also interacted with the key hinge region residue Met156 of ROCK I. Further, molecular dynamics (MD) simulation revealed that the protein-ligand complexes were stable and maintained the hydrogen bond with Met156 throughout the MD run. The promising in silico outcomes suggest that the designed compounds could be suitable anti-cancer leads that need to be synthesized and tested in various cancer cell lines. Graphical abstract.
Collapse
Affiliation(s)
- Hemant Arya
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, 605014, India
| | - Mohane Selvaraj Coumar
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, 605014, India.
| |
Collapse
|
6
|
Vanhaelen Q, Lin YC, Zhavoronkov A. The Advent of Generative Chemistry. ACS Med Chem Lett 2020; 11:1496-1505. [PMID: 32832015 PMCID: PMC7429972 DOI: 10.1021/acsmedchemlett.0c00088] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.
Collapse
Affiliation(s)
- Quentin Vanhaelen
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Yen-Chu Lin
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
- Insilico
Taiwan, Taipei City 115, Taiwan, R.O.C
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| |
Collapse
|
7
|
Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
Collapse
Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| |
Collapse
|
8
|
Friedrich L, Byrne R, Treder A, Singh I, Bauer C, Gudermann T, Mederos Y Schnitzler M, Storch U, Schneider G. Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics. ChemMedChem 2020; 15:566-570. [PMID: 32162837 DOI: 10.1002/cmdc.202000017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/09/2020] [Indexed: 12/22/2022]
Abstract
Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.
Collapse
Affiliation(s)
- Lukas Friedrich
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Ryan Byrne
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Aaron Treder
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany
| | - Inderjeet Singh
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany
| | - Christoph Bauer
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Thomas Gudermann
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Biedersteiner Strasse 29, 80802, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Max-Lebsche-Platz 31, 81377, Munich, Germany
| | - Michael Mederos Y Schnitzler
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Biedersteiner Strasse 29, 80802, Munich, Germany
| | - Ursula Storch
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,Institute for Cardiovascular Prevention (IPEK), Ludwig Maximilians University of Munich, Pettenkoferstrasse 8a & 9, 80336, Munich, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| |
Collapse
|
9
|
Cardoso-Silva J, Papageorgiou LG, Tsoka S. Network-based piecewise linear regression for QSAR modelling. J Comput Aided Mol Des 2019; 33:831-844. [PMID: 31628660 PMCID: PMC6825651 DOI: 10.1007/s10822-019-00228-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/28/2019] [Indexed: 02/07/2023]
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.
Collapse
Affiliation(s)
- Jonathan Cardoso-Silva
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, UK
| | - Lazaros G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London, WC1E 7JE, UK
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, UK.
| |
Collapse
|
10
|
Skalic M, Jiménez J, Sabbadin D, De Fabritiis G. Shape-Based Generative Modeling for de Novo Drug Design. J Chem Inf Model 2019; 59:1205-1214. [PMID: 30762364 DOI: 10.1021/acs.jcim.8b00706] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of the de novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound, followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens. The generative design of novel scaffolds and functional groups can cover unexplored regions of chemical space that still possess lead-like properties.
Collapse
Affiliation(s)
- Miha Skalic
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88 , 08003 Barcelona , Spain
| | - José Jiménez
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88 , 08003 Barcelona , Spain
| | - Davide Sabbadin
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88 , 08003 Barcelona , Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88 , 08003 Barcelona , Spain.,Acellera , Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88 , 08003 Barcelona , Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23 , 08010 Barcelona , Spain
| |
Collapse
|
11
|
Athanasiou C, Cournia Z. From Computers to Bedside: Computational Chemistry Contributing to FDA Approval. BIOMOLECULAR SIMULATIONS IN STRUCTURE-BASED DRUG DISCOVERY 2018. [DOI: 10.1002/9783527806836.ch7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Christina Athanasiou
- Biomedical Research Foundation; Academy of Athens; 4 Soranou Ephessiou 11527 Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation; Academy of Athens; 4 Soranou Ephessiou 11527 Athens Greece
| |
Collapse
|
12
|
Allen WJ, Fochtman BC, Balius TE, Rizzo RC. Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets. J Comput Chem 2017; 38:2641-2663. [PMID: 28940386 DOI: 10.1002/jcc.25052] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 08/03/2017] [Indexed: 12/12/2022]
Abstract
De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- William J Allen
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794
| | - Brian C Fochtman
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, 11794
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, 94158
| | - Robert C Rizzo
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794.,Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, 11794.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, 11794
| |
Collapse
|
13
|
Scheepstra M, Andrei SA, Unver MY, Hirsch AKH, Leysen S, Ottmann C, Brunsveld L, Milroy LG. Designed Spiroketal Protein Modulation. Angew Chem Int Ed Engl 2017. [DOI: 10.1002/ange.201612504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Marcel Scheepstra
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS); Department of Biomedical Engineering; Technische Universiteit Eindhoven; Den Dolech 2 5612 AZ Eindhoven The Netherlands
| | - Sebastian A. Andrei
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS); Department of Biomedical Engineering; Technische Universiteit Eindhoven; Den Dolech 2 5612 AZ Eindhoven The Netherlands
| | - M. Yagiz Unver
- Stratingh Institute for Chemistry; University of Groningen; Nijenborgh 7 9747AG Groningen The Netherlands
| | - Anna K. H. Hirsch
- Stratingh Institute for Chemistry; University of Groningen; Nijenborgh 7 9747AG Groningen The Netherlands
| | - Seppe Leysen
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS); Department of Biomedical Engineering; Technische Universiteit Eindhoven; Den Dolech 2 5612 AZ Eindhoven The Netherlands
| | - Christian Ottmann
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS); Department of Biomedical Engineering; Technische Universiteit Eindhoven; Den Dolech 2 5612 AZ Eindhoven The Netherlands
- Department of Chemistry; University of Duisburg-Essen; Universitätstr. 7 45141 Essen Germany
| | - Luc Brunsveld
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS); Department of Biomedical Engineering; Technische Universiteit Eindhoven; Den Dolech 2 5612 AZ Eindhoven The Netherlands
| | - Lech-Gustav Milroy
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS); Department of Biomedical Engineering; Technische Universiteit Eindhoven; Den Dolech 2 5612 AZ Eindhoven The Netherlands
| |
Collapse
|
14
|
Scheepstra M, Andrei SA, Unver MY, Hirsch AKH, Leysen S, Ottmann C, Brunsveld L, Milroy LG. Designed Spiroketal Protein Modulation. Angew Chem Int Ed Engl 2017; 56:5480-5484. [PMID: 28407400 PMCID: PMC5435924 DOI: 10.1002/anie.201612504] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 02/17/2017] [Indexed: 02/03/2023]
Abstract
Spiroketals are structural motifs found in many biologically active natural products, which has stimulated considerable efforts toward their synthesis and interest in their use as drug lead compounds. Despite this, the use of spiroketals, and especially bisbenzanulated spiroketals, in a structure-based drug discovery setting has not been convincingly demonstrated. Herein, we report the rational design of a bisbenzannulated spiroketal that potently binds to the retinoid X receptor (RXR) thereby inducing partial co-activator recruitment. We solved the crystal structure of the spiroketal-hRXRα-TIF2 ternary complex, and identified a canonical allosteric mechanism as a possible explanation for the partial agonist behavior of our spiroketal. Our co-crystal structure, the first of a designed spiroketal-protein complex, suggests that spiroketals can be designed to selectively target other nuclear receptor subtypes.
Collapse
Affiliation(s)
- Marcel Scheepstra
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands
| | - Sebastian A Andrei
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands
| | - M Yagiz Unver
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 7, 9747AG, Groningen, The Netherlands
| | - Anna K H Hirsch
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 7, 9747AG, Groningen, The Netherlands
| | - Seppe Leysen
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands
| | - Christian Ottmann
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands.,Department of Chemistry, University of Duisburg-Essen, Universitätstr. 7, 45141, Essen, Germany
| | - Luc Brunsveld
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands
| | - Lech-Gustav Milroy
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands
| |
Collapse
|
15
|
Schneider P, Schneider G. De-orphaning the marine natural product (±)-marinopyrrole A by computational target prediction and biochemical validation. Chem Commun (Camb) 2017; 53:2272-2274. [DOI: 10.1039/c6cc09693j] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
A machine-learning method led to the discovery of the macromolecular targets of the natural anticancer compound marinopyrrol A.
Collapse
Affiliation(s)
- P. Schneider
- Department of Chemistry and Applied Biosciences
- Swiss Federal Institute of Technology (ETH)
- Zurich
- Switzerland
- inSili.com LLC
| | - G. Schneider
- Department of Chemistry and Applied Biosciences
- Swiss Federal Institute of Technology (ETH)
- Zurich
- Switzerland
| |
Collapse
|
16
|
|
17
|
Button AL, Hiss JA, Schneider P, Schneider G. Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction. Mol Inform 2016; 36. [PMID: 27643811 DOI: 10.1002/minf.201600110] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 08/27/2016] [Indexed: 11/10/2022]
Abstract
Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.
Collapse
Affiliation(s)
- Alexander L Button
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| | - Petra Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland.,inSili.com LLC, Segantinisteig 3, CH-, 8049, Zurich, Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| |
Collapse
|
18
|
Abstract
How to design a ligand to bind multiple targets, rather than to a single target, is the focus of this review. Rational polypharmacology draws on knowledge that is both broad ranging and hierarchical. Computer-aided multitarget ligand design methods are described according to their nested knowledge level. Ligand-only and then receptor-ligand strategies are first described; followed by the metabolic network viewpoint. Subsequently strategies that view infectious diseases as multigenomic targets are discussed, and finally the disease level interpretation of medicinal therapy is considered. As yet there is no consensus on how best to proceed in designing a multitarget ligand. The current methodologies are bought together in an attempt to give a practical overview of how polypharmacology design might be best initiated.
Collapse
|
19
|
Abstract
Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.
Collapse
Affiliation(s)
- Petra Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) , Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.,inSili.com LLC , Segantinisteig 3, 8049 Zürich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) , Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
| |
Collapse
|
20
|
Kalathiya U, Padariya M, Baginski M. Identification of 1H-indene-(1,3,5,6)-tetrol derivatives as potent pancreatic lipase inhibitors using molecular docking and molecular dynamics approach. Biotechnol Appl Biochem 2015; 63:765-778. [DOI: 10.1002/bab.1432] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 08/06/2015] [Indexed: 12/28/2022]
Affiliation(s)
- Umesh Kalathiya
- Department of Pharmaceutical Technology and Biochemistry; Faculty of Chemistry; Gdansk University of Technology; Gdansk Poland
| | - M. Padariya
- Department of Pharmaceutical Technology and Biochemistry; Faculty of Chemistry; Gdansk University of Technology; Gdansk Poland
| | - M. Baginski
- Department of Pharmaceutical Technology and Biochemistry; Faculty of Chemistry; Gdansk University of Technology; Gdansk Poland
| |
Collapse
|
21
|
Abstract
Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.
Collapse
Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, Bonn, D-53113, Germany
| |
Collapse
|
22
|
Other Related Techniques. UNDERSTANDING THE BASICS OF QSAR FOR APPLICATIONS IN PHARMACEUTICAL SCIENCES AND RISK ASSESSMENT 2015. [PMCID: PMC7149793 DOI: 10.1016/b978-0-12-801505-6.00010-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.
Collapse
|
23
|
|
24
|
Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci U S A 2014; 111:4067-72. [PMID: 24591595 DOI: 10.1073/pnas.1320001111] [Citation(s) in RCA: 171] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibrate-related compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.
Collapse
|
25
|
Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G. Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules. Mol Inform 2013; 32:133-138. [PMID: 23956801 PMCID: PMC3743170 DOI: 10.1002/minf.201200141] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Accepted: 01/18/2013] [Indexed: 02/04/2023]
Affiliation(s)
- Michael Reutlinger
- ETH, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences Wolfgang-Pauli-Str. 10, CH-8093 Zurich, Switzerland fax: +41 44 633 13 79, tel: +41 44 633 74 38
| | | | | | | | | | | | | |
Collapse
|