1
|
Nippa DF, Atz K, Müller AT, Wolfard J, Isert C, Binder M, Scheidegger O, Konrad DB, Grether U, Martin RE, Schneider G. Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening. Commun Chem 2023; 6:256. [PMID: 37985850 PMCID: PMC10661846 DOI: 10.1038/s42004-023-01047-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.
Collapse
Affiliation(s)
- David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Kenneth Atz
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Alex T Müller
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Jens Wolfard
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Clemens Isert
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Martin Binder
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Oliver Scheidegger
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - David B Konrad
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany.
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Rainer E Martin
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
| |
Collapse
|
2
|
Lee ML, Farag S, Del Cid JS, Bashore C, Hallenbeck KK, Gobbi A, Cunningham CN. Identification of Macrocyclic Peptide Families from Combinatorial Libraries Containing Noncanonical Amino Acids Using Cheminformatics and Bioinformatics Inspired Clustering. ACS Chem Biol 2023; 18:1425-1434. [PMID: 37220419 PMCID: PMC10278063 DOI: 10.1021/acschembio.3c00159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/10/2023] [Indexed: 05/25/2023]
Abstract
In the past decade, macrocyclic peptides gained increasing interest as a new therapeutic modality to tackle intracellular and extracellular therapeutic targets that had been previously classified as "undruggable". Several technological advances have made discovering macrocyclic peptides against these targets possible: 1) the inclusion of noncanonical amino acids (NCAAs) into mRNA display, 2) increased availability of next generation sequencing (NGS), and 3) improvements in rapid peptide synthesis platforms. This type of directed-evolution based screening can produce large numbers of potential hit sequences given that DNA sequencing is the functional output of this platform. The current standard for selecting hit peptides from these selections for downstream follow-up relies on the frequency counting and sorting of unique peptide sequences which can result in the generation of false negatives due to technical reasons including low translation efficiency or other experimental factors. To overcome our inability to detect weakly enriched peptide sequences among our large data sets, we wanted to develop a clustering method that would enable the identification of peptide families. Unfortunately, utilizing traditional clustering algorithms, such as ClustalW, is not possible for this technology due to the incorporation of NCAAs in these libraries. Therefore, we developed a new atomistic clustering method with a Pairwise Aligned Peptide (PAP) chemical similarity metric to perform sequence alignments and identify macrocyclic peptide families. With this method, low enriched peptides, including isolated sequences (singletons), can now be clustered into families providing a comprehensive analysis of NGS data resulting from macrocycle discovery selections. Additionally, upon identification of a hit peptide with the desired activity, this clustering algorithm can be used to identify derivatives from the initial data set for structure-activity relationship (SAR) analysis without requiring additional selection experiments.
Collapse
Affiliation(s)
- Man-Ling Lee
- Discovery
Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Sherif Farag
- Discovery
Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Joselyn S. Del Cid
- Peptide
Therapeutics, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Charlene Bashore
- Biological
Chemistry, Genentech Inc. 1 DNA Way, South San Francisco, California 94080, United States
| | - Kenneth K. Hallenbeck
- Peptide
Therapeutics, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Alberto Gobbi
- Discovery
Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Christian N. Cunningham
- Peptide
Therapeutics, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| |
Collapse
|
3
|
Steffek M, Helgason E, Popovych N, Rougé L, Bruning JM, Li KS, Burdick DJ, Cai J, Crawford T, Xue J, Decurtins W, Fang C, Grubers F, Holliday MJ, Langley A, Petersen A, Satz AL, Song A, Stoffler D, Strebel Q, Tom JYK, Skelton N, Staben ST, Wichert M, Mulvihill MM, Dueber EC. A Multifaceted Hit-Finding Approach Reveals Novel LC3 Family Ligands. Biochemistry 2023; 62:633-644. [PMID: 34985287 DOI: 10.1021/acs.biochem.1c00682] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Autophagy-related proteins (Atgs) drive the lysosome-mediated degradation pathway, autophagy, to enable the clearance of dysfunctional cellular components and maintain homeostasis. In humans, this process is driven by the mammalian Atg8 (mAtg8) family of proteins comprising the LC3 and GABARAP subfamilies. The mAtg8 proteins play essential roles in the formation and maturation of autophagosomes and the capture of specific cargo through binding to the conserved LC3-interacting region (LIR) sequence within target proteins. Modulation of interactions of mAtg8 with its target proteins via small-molecule ligands would enable further interrogation of their function. Here we describe unbiased fragment and DNA-encoded library (DEL) screening approaches for discovering LC3 small-molecule ligands. Both strategies resulted in compounds that bind to LC3, with the fragment hits favoring a conserved hydrophobic pocket in mATG8 proteins, as detailed by LC3A-fragment complex crystal structures. Our findings demonstrate that the malleable LIR-binding surface can be readily targeted by fragments; however, rational design of additional interactions to drive increased affinity proved challenging. DEL libraries, which combine small, fragment-like building blocks into larger scaffolds, yielded higher-affinity binders and revealed an unexpected potential for reversible, covalent ligands. Moreover, DEL hits identified possible vectors for synthesizing fluorescent probes or bivalent molecules for engineering autophagic degradation of specific targets.
Collapse
Affiliation(s)
- Micah Steffek
- Biochemical and Cellular Pharmacology, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Elizabeth Helgason
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Nataliya Popovych
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Lionel Rougé
- Structure Biology, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - John M Bruning
- Biochemical and Cellular Pharmacology, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Ke Sherry Li
- Biochemical and Cellular Pharmacology, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Daniel J Burdick
- Chemistry Departments, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Jianping Cai
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Terry Crawford
- Chemistry Departments, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Jing Xue
- Biochemical and Cellular Pharmacology, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Willy Decurtins
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Chunlin Fang
- WuXi AppTec (Wuhan) Company, Ltd., No. 666 GaoXin Road, WuHan East Lake High-tech Development Zone, Hubei 430075, China
| | - Felix Grubers
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Michael J Holliday
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Allyson Langley
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Ann Petersen
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Alexander Lee Satz
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Aimin Song
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Daniel Stoffler
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Quentin Strebel
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Jeffrey Y K Tom
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Nicholas Skelton
- Chemistry Departments, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Steven T Staben
- Chemistry Departments, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Moreno Wichert
- Roche Pharma Research and Early Development, Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Melinda M Mulvihill
- Biochemical and Cellular Pharmacology, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Erin C Dueber
- Early Discovery Biochemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| |
Collapse
|
4
|
Johnson C, Anger LT, Benigni R, Bower D, Bringezu F, Crofton KM, Cronin MT, Cross KP, Dettwiler M, Frericks M, Melnikov F, Miller S, Roberts DW, Suarez-Rodriguez D, Roncaglioni A, Lo Piparo E, Tice RR, Zwickl C, Myatt GJ. Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100204. [PMID: 35368849 PMCID: PMC8967148 DOI: 10.1016/j.comtox.2021.100204] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within Integrated approaches to testing and assessment (IATA).
Collapse
Affiliation(s)
- Candice Johnson
- Instem, 1393 Dublin Rd, Columbus, OH 43215, USA,Corresponding author.
(C. Johnson)
| | | | | | - David Bower
- Instem, 1393 Dublin Rd, Columbus, OH 43215, USA
| | | | | | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Liverpool, L3 3AF, UK
| | | | - Magdalena Dettwiler
- Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, 4123
Allschwill, Switzerland
| | - Markus Frericks
- BASF SE, APD/ET, Li 444, Speyerer St 2, 67117
Limburgerhof, Germany
| | - Fjodor Melnikov
- Genentech, Inc., 1 DNA Way, South San Francisco, CA,
94080, USA
| | | | - David W. Roberts
- School of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Liverpool, L3 3AF, UK
| | | | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology,
Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche
Mario Negri IRCCS, Milan, Italy
| | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research,
Lausanne, Switzerland
| | | | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN
46229, USA
| | | |
Collapse
|
5
|
Ståhl N, Falkman G, Karlsson A, Mathiason G, Boström J. Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design. J Chem Inf Model 2019; 59:3166-3176. [PMID: 31273995 DOI: 10.1021/acs.jcim.9b00325] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.
Collapse
Affiliation(s)
- Niclas Ståhl
- School of Informatics , University of Skövde , 541 28 Skövde , Sweden
| | - Göran Falkman
- School of Informatics , University of Skövde , 541 28 Skövde , Sweden
| | | | - Gunnar Mathiason
- School of Informatics , University of Skövde , 541 28 Skövde , Sweden
| | - Jonas Boström
- Medicinal Chemistry, Early Cardiovascular, Renal and Metabolism, R&D BioPharmaceuticals , AstraZeneca , 431 83 Mölndal , Sweden
| |
Collapse
|
6
|
Lee ML, Aliagas I, Feng JA, Gabriel T, O’Donnell TJ, Sellers BD, Wiswedel B, Gobbi A. chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery. J Cheminform 2017; 9:38. [PMID: 29086196 PMCID: PMC5468363 DOI: 10.1186/s13321-017-0228-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Accepted: 06/01/2017] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Analyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemalot is a set of command line programs with a wide range of functionalities for cheminformatics. The chemalot_knime package allows command line programs that read and write SD files from stdin and to stdout to be wrapped into KNIME nodes. The combination of chemalot and chemalot_knime not only facilitates the compilation and maintenance of sequences of command line programs but also allows KNIME workflows to take advantage of the compute power of a LINUX cluster. RESULTS Use of the command line programs is demonstrated in three different workflow examples: (1) A workflow to create a data file with project-relevant data for structure-activity or property analysis and other type of investigations, (2) The creation of a quantitative structure-property-relationship model using the command line programs via KNIME nodes, and (3) The analysis of strain energy in small molecule ligand conformations from the Protein Data Bank database. CONCLUSIONS The chemalot and chemalot_knime packages provide lightweight and powerful tools for many tasks in cheminformatics. They are easily integrated with other open source and commercial command line tools and can be combined to build new and even more powerful tools. The chemalot_knime package facilitates the generation and maintenance of user-defined command line workflows, taking advantage of the graphical design capabilities in KNIME. Graphical abstract Example KNIME workflow with chemalot nodes and the corresponding command line pipe.
Collapse
Affiliation(s)
- Man-Ling Lee
- Discovery Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080 USA
| | - Ignacio Aliagas
- Discovery Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080 USA
| | - Jianwen A. Feng
- Denali Therapeutics, 151 Oyster Point Blvd, South San Francisco, CA 94080 USA
| | - Thomas Gabriel
- KNIME.com AG, Technoparkstrasse 1, 8005 Zurich, Switzerland
| | | | - Benjamin D. Sellers
- Discovery Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080 USA
| | - Bernd Wiswedel
- KNIME.com AG, Technoparkstrasse 1, 8005 Zurich, Switzerland
| | - Alberto Gobbi
- Discovery Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080 USA
| |
Collapse
|
7
|
Tsui V, Ortwine DF, Blaney JM. Enabling drug discovery project decisions with integrated computational chemistry and informatics. J Comput Aided Mol Des 2016; 31:287-291. [DOI: 10.1007/s10822-016-9988-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 10/22/2016] [Indexed: 01/24/2023]
|
8
|
Selwa E, Martiny VY, Iorga BI. Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets. J Comput Aided Mol Des 2016; 30:829-839. [DOI: 10.1007/s10822-016-9983-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 09/28/2016] [Indexed: 12/15/2022]
|
9
|
Leal W, Llanos EJ, Restrepo G, Suárez CF, Patarroyo ME. How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity. J Cheminform 2016; 8:4. [PMID: 26816532 PMCID: PMC4727313 DOI: 10.1186/s13321-016-0114-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 01/08/2016] [Indexed: 11/24/2022] Open
Abstract
Background Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies. Results Representing dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets. Conclusions Our approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Four cluster contrast functions identifying statistically sound clusters within dendrograms considering ties in proximity ![]()
Collapse
Affiliation(s)
- Wilmer Leal
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia ; Laboratorio de Química Teórica, Universidad de Pamplona, Pamplona, Colombia
| | - Eugenio J Llanos
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia ; Laboratorio de Química Teórica, Universidad de Pamplona, Pamplona, Colombia ; Corporación SCIO, Bogotá, Colombia
| | - Guillermo Restrepo
- Laboratorio de Química Teórica, Universidad de Pamplona, Pamplona, Colombia ; Bioinformatics Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany
| | - Carlos F Suárez
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia ; Universidad del Rosario, Bogotá, Colombia
| | - Manuel Elkin Patarroyo
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia ; Universidad Nacional de Colombia, Bogotá, Colombia
| |
Collapse
|