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1.
Methods Mol Biol ; 2390: 207-232, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34731471

RESUMO

Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well in various experimental scenarios. In this study, we provide a hands-on introduction to chemical language modeling. A technique based on recurrent neural networks is discussed in detail, together with a step-by-step guide to applying this AI method for focused compound library design. The program code is freely available at URL: github.com/ETHmodlab/de_novo_design_RNN .


Assuntos
Inteligência Artificial , Idioma , Desenho de Fármacos , Modelos Químicos , Redes Neurais de Computação , Estudos Prospectivos
2.
J Chem Inf Model ; 61(10): 5256-5268, 2021 10 25.
Artigo em Inglês | MEDLINE | ID: mdl-34597510

RESUMO

African and American trypanosomiases are estimated to affect several million people across the world, with effective treatments distinctly lacking. New, ideally oral, treatments with higher efficacy against these diseases are desperately needed. Peroxisomal import matrix (PEX) proteins represent a very interesting target for structure- and ligand-based drug design. The PEX5-PEX14 protein-protein interface in particular has been highlighted as a target, with inhibitors shown to disrupt essential cell processes in trypanosomes, leading to cell death. In this work, we present a drug development campaign that utilizes the synergy between structural biology, computer-aided drug design, and medicinal chemistry in the quest to discover and develop new potential compounds to treat trypanosomiasis by targeting the PEX14-PEX5 interaction. Using the structure of the known lead compounds discovered by Dawidowski et al. as the template for a chemically advanced template search (CATS) algorithm, we performed scaffold-hopping to obtain a new class of compounds with trypanocidal activity, based on 2,3,4,5-tetrahydrobenzo[f][1,4]oxazepines chemistry. The initial compounds obtained were taken forward to a first round of hit-to-lead optimization by synthesis of derivatives, which show activities in the range of low- to high-digit micromolar IC50 in the in vitro tests. The NMR measurements confirm binding to PEX14 in solution, while immunofluorescent microscopy indicates disruption of protein import into the glycosomes, indicating that the PEX14-PEX5 protein-protein interface was successfully disrupted. These studies result in development of a novel scaffold for future lead optimization, while ADME testing gives an indication of further areas of improvement in the path from lead molecules toward a new drug active against trypanosomes.


Assuntos
Oxazepinas , Tripanossomicidas , Desenho Assistido por Computador , Proteínas de Membrana/metabolismo , Receptor 1 de Sinal de Orientação para Peroxissomos , Receptores Citoplasmáticos e Nucleares , Proteínas Repressoras/metabolismo , Tripanossomicidas/farmacologia
3.
Anal Chem ; 93(39): 13342-13350, 2021 10 05.
Artigo em Inglês | MEDLINE | ID: mdl-34546705

RESUMO

Fast and efficient handling of ligands and biological targets are required in bioaffinity screening based on native electrospray ionization mass spectrometry (ESI-MS). We use a prototype microfluidic autosampler, called the "gap sampler", to sequentially mix and electrospray individual small molecule ligands together with a target protein and compare the screening results with data from thermal shift assay and surface plasmon resonance. In a first round, all three techniques were used for a screening of 110 ligands against bovine carbonic anhydrase II, which resulted in five mutual hits and some false positives with ESI-MS presumably due to the high ligand concentration or interferences from dimethyl sulfoxide. In a second round, 33 compounds were screened in lower concentrations and in a less complex matrix, resulting in only true positives with ESI-MS. Within a cycle time of 30 s, dissociation constants were determined within an order of magnitude accuracy consuming only 5 pmol of ligand and less than 15 pmol of protein per screened compound. In a third round, dissociation constants of five compounds were accurately determined in a titration experiment. Thus, the gap sampler can rapidly and efficiently be used for high-throughput screening.


Assuntos
Pesquisa , Espectrometria de Massas por Ionização por Electrospray , Animais , Bovinos
5.
Chem Soc Rev ; 50(16): 9121-9151, 2021 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-34212944

RESUMO

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.


Assuntos
COVID-19/tratamento farmacológico , Simulação por Computador , Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Antivirais/uso terapêutico , COVID-19/virologia , Ensaios Clínicos como Assunto , Humanos , Pandemias , SARS-CoV-2/efeitos dos fármacos
6.
Proc Natl Acad Sci U S A ; 118(31)2021 08 03.
Artigo em Inglês | MEDLINE | ID: mdl-34326250

RESUMO

G protein-coupled receptors (GPCRs) are important pharmaceutical targets for the treatment of a broad spectrum of diseases. Although there are structures of GPCRs in their active conformation with bound ligands and G proteins, the detailed molecular interplay between the receptors and their signaling partners remains challenging to decipher. To address this, we developed a high-sensitivity, high-throughput matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) method to interrogate the first stage of signal transduction. GPCR-G protein complex formation is detected as a proxy for the effect of ligands on GPCR conformation and on coupling selectivity. Over 70 ligand-GPCR-partner protein combinations were studied using as little as 1.25 pmol protein per sample. We determined the selectivity profile and binding affinities of three GPCRs (rhodopsin, beta-1 adrenergic receptor [ß1AR], and angiotensin II type 1 receptor) to engineered Gα-proteins (mGs, mGo, mGi, and mGq) and nanobody 80 (Nb80). We found that GPCRs in the absence of ligand can bind mGo, and that the role of the G protein C terminus in GPCR recognition is receptor-specific. We exemplified our quantification method using ß1AR and demonstrated the allosteric effect of Nb80 binding in assisting displacement of nadolol to isoprenaline. We also quantified complex formation with wild-type heterotrimeric Gαißγ and ß-arrestin-1 and showed that carvedilol induces an increase in coupling of ß-arrestin-1 and Gαißγ to ß1AR. A normalization strategy allows us to quantitatively measure the binding affinities of GPCRs to partner proteins. We anticipate that this methodology will find broad use in screening and characterization of GPCR-targeting drugs.

7.
Sci Adv ; 7(24)2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34117066

RESUMO

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

8.
Adv Sci (Weinh) ; 8(16): e2100832, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34176236

RESUMO

The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.

9.
Angew Chem Int Ed Engl ; 60(35): 19477-19482, 2021 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-34165856

RESUMO

Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. Herein, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORγ. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.


Assuntos
Automação , Produtos Biológicos/farmacologia , Desenho de Fármacos , Receptores do Ácido Retinoico/agonistas , Algoritmos , Produtos Biológicos/síntese química , Produtos Biológicos/química , Humanos , Ligantes , Estrutura Molecular
10.
Expert Opin Drug Discov ; 16(9): 949-959, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-33779453

RESUMO

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.

11.
Methods Mol Biol ; 2266: 11-35, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33759119

RESUMO

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 .


Assuntos
Quimioinformática/métodos , Descoberta de Drogas/métodos , Bibliotecas de Moléculas Pequenas/química , Desenho de Fármacos , Software
12.
J Chem Inf Model ; 61(3): 1083-1094, 2021 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-33629843

RESUMO

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand-target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically relevant endpoints.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Descoberta de Drogas , Ligantes
13.
Redox Biol ; 38: 101773, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33197771

RESUMO

α-tocopherol transfer protein (TTP) was previously reported to self-aggregate into 24-meric spheres (α-TTPS) and to possess transcytotic potency across mono-layers of human umbilical vein endothelial cells (HUVECs). In this work, we describe the characterisation of a functional TTP variant with its vitamer selectivity shifted towards γ-tocopherol. The shift was obtained by introducing an alanine to leucine substitution into the substrate-binding pocket at position 156 through site directed mutagenesis. We report here the X-ray crystal structure of the γ-tocopherol specific particle (γ-TTPS) at 2.24 Å resolution. γ-TTPS features full functionality compared to its α-tocopherol specific parent including self-aggregation potency and transcytotic activity in trans-well experiments using primary HUVEC cells. The impact of the A156L mutation on TTP function is quantified in vitro by measuring the affinity towards γ-tocopherol through micro-differential scanning calorimetry and by determining its ligand-transfer activity. Finally, cell culture experiments using adherently grown HUVEC cells indicate that the protomers of γ-TTP, in contrast to α-TTP, do not counteract cytokine-mediated inflammation at a transcriptional level. Our results suggest that the A156L substitution in TTP is fully functional and has the potential to pave the way for further experiments towards the understanding of α-tocopherol homeostasis in humans.


Assuntos
Células Endoteliais , gama-Tocoferol , Humanos , Ligantes , Mutagênese Sítio-Dirigida , Vitamina E , alfa-Tocoferol
14.
Biochemistry ; 59(39): 3772-3781, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-32936629

RESUMO

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties de novo. Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of Staphylococcus aureus, and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical de novo peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.


Assuntos
Antibacterianos/química , Antibacterianos/farmacologia , Peptídeos Catiônicos Antimicrobianos/química , Peptídeos Catiônicos Antimicrobianos/farmacologia , Staphylococcus aureus/efeitos dos fármacos , Sequência de Aminoácidos , Desenho de Fármacos , Células HEK293 , Humanos , Simulação de Dinâmica Molecular , Conformação Proteica em alfa-Hélice , Infecções Estafilocócicas/tratamento farmacológico , Infecções Estafilocócicas/microbiologia , Staphylococcus aureus/crescimento & desenvolvimento
15.
Sci Rep ; 10(1): 10563, 2020 06 29.
Artigo em Inglês | MEDLINE | ID: mdl-32601479

RESUMO

Helicobacter pylori (H. pylori) secretes the chaperone and serine protease high temperature requirement A (HtrA) that cleaves gastric epithelial cell surface proteins to disrupt the epithelial integrity and barrier function. First inhibitory lead structures have demonstrated the essential role of HtrA in H. pylori physiology and pathogenesis. Comprehensive drug discovery techniques allowing high-throughput screening are now required to develop effective compounds. Here, we designed a novel fluorescence resonance energy transfer (FRET) peptide derived from a gel-based label-free proteomic approach (direct in-gel profiling of protease specificity) as a valuable substrate for H. pylori HtrA. Since serine proteases are often sensitive to metal ions, we investigated the influence of different divalent ions on the activity of HtrA. We identified Zn++ and Cu++ ions as inhibitors of H. pylori HtrA activity, as monitored by in vitro cleavage experiments using casein or E-cadherin as substrates and in the FRET peptide assay. Putative binding sites for Zn++ and Cu++ were then analyzed in thermal shift and microscale thermophoresis assays. The findings of this study will contribute to the development of novel metal ion-dependent protease inhibitors, which might help to fight bacterial infections.


Assuntos
Proteínas de Bactérias/antagonistas & inibidores , Avaliação Pré-Clínica de Medicamentos/métodos , Transferência Ressonante de Energia de Fluorescência/métodos , Proteínas de Bactérias/metabolismo , Caderinas/metabolismo , Cobre/metabolismo , Infecções por Helicobacter/tratamento farmacológico , Infecções por Helicobacter/metabolismo , Helicobacter pylori/metabolismo , Chaperonas Moleculares/metabolismo , Peptídeos/metabolismo , Proteômica/métodos , Serina Endopeptidases/metabolismo , Serina Proteases/metabolismo , Zinco/metabolismo
16.
Artigo em Inglês | MEDLINE | ID: mdl-32513799

RESUMO

Several cationic amphiphilic drugs (CADs) have been found to inhibit cell entry of filoviruses and other enveloped viruses. Structurally unrelated CADs may have antiviral activity, yet the underlying common mechanism and structure-activity relationship are incompletely understood. We aimed to understand how widespread antiviral activity is among CADs and which structural and physico-chemical properties are linked to entry inhibition. We measured inhibition of Marburg virus pseudoparticle (MARVpp) cell entry by 45 heterogeneous and mostly FDA-approved CADs and cytotoxicity in EA.hy926 cells. We analyzed correlation of antiviral activity with four chemical properties: pKa, hydrophobicity (octanol/water partitioning coefficient; ClogP), molecular weight, and distance between the basic group and hydrophobic ring structures. Additionally, we quantified drug-induced phospholipidosis (DIPL) of a CAD subset by flow cytometry. Structurally similar compounds (derivatives) and those with similar chemical properties but unrelated structures (analogues) to those of strong inhibitors were obtained by two in silico similarity search approaches and tested for antiviral activity. Overall, 11 out of 45 (24%) CADs inhibited MARVpp by 40% or more. The strongest antiviral compounds were dronedarone, triparanol, and quinacrine. Structure-activity relationship studies revealed highly significant correlations between antiviral activity, hydrophobicity (ClogP > 4), and DIPL. Moreover, pKa and intramolecular distance between hydrophobic and hydrophilic moieties correlated with antiviral activity but to a lesser extent. We also showed that in contrast to analogues, derivatives had antiviral activity similar to that of the seed compound dronedarone. Overall, one-quarter of CADs inhibit MARVpp entry in vitro, and antiviral activity of CADs mostly relies on their hydrophobicity yet is promoted by the individual structure.


Assuntos
Filoviridae , Marburgvirus , Preparações Farmacêuticas , Antivirais/farmacologia , Internalização do Vírus
17.
ChemMedChem ; 15(7): 566-570, 2020 04 03.
Artigo em Inglês | MEDLINE | ID: mdl-32162837

RESUMO

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.


Assuntos
Desenho de Fármacos , Sesquiterpenos de Guaiano/farmacologia , Canais de Cátion TRPC/antagonistas & inibidores , Canais de Cátion TRPM/antagonistas & inibidores , Células HEK293 , Humanos , Modelos Moleculares , Estrutura Molecular , Sesquiterpenos de Guaiano/síntese química , Sesquiterpenos de Guaiano/química , Canais de Cátion TRPC/metabolismo , Canais de Cátion TRPM/metabolismo
18.
MAbs ; 12(1): 1736975, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32167012

RESUMO

Minor changes in the quality of biologically manufactured monoclonal antibodies (mAbs) can affect their bioactivity and efficacy. One of the most important variations concerns the N-glycosylation pattern, which directly affects an anti-tumor mechanism called antibody-dependent cell-meditated cytotoxicity (ADCC). Thus, careful engineering of mAbs is expected to enhance both protein-receptor binding and ADCC. The specific aim of this study is to evaluate the influence of terminal carbohydrates within the Fc region on the interaction with the FcγRIIIa/CD16a receptor in native and label-free conditions. The single mAb molecule comprises variants with minimal and maximal galactosylation, as well as α2,3 and α2,6-sialic acid isomers. Here, we apply native electrospray ionization mass spectrometry to determine the solution-phase antibody-receptor equilibria and by using temperature-controlled nanoelectrospray, a thermal stability of the complex is examined. Based on these, we prove that the galactosylation of a fucosylated Fc region increases the binding to CD16a 1.5-fold when compared with the non-galactosylated variant. The α2,6-sialylation has no significant effect on the binding, whereas the α2,3-sialylation decreases it 1.72-fold. In line with expectation, the galactoslylated and α2,6-sialylated mAb:CD16a complex exhibit higher thermal stability when measured in the temperature gradient from 20 to 50°C. The similar binding pattern is observed based on surface plasmon resonance analysis and immunofluorescence staining using natural killer cells. The results of our study provide new insight into N-glycosylation-based interaction of the mAb:CD16a complex.


Assuntos
Anticorpos Monoclonais/química , Engenharia Celular/métodos , Fragmentos Fc das Imunoglobulinas/química , Receptores de IgG/imunologia , Espectrometria de Massas por Ionização por Electrospray/métodos , Animais , Glicosilação , Humanos
19.
J Chem Inf Model ; 60(3): 1175-1183, 2020 03 23.
Artigo em Inglês | MEDLINE | ID: mdl-31904964

RESUMO

Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL.


Assuntos
Redes Neurais de Computação
20.
Mol Inform ; 39(9): e2000109, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-33448694

RESUMO

Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.


Assuntos
Inibidores de Proteínas Quinases/farmacologia , Proteínas Proto-Oncogênicas c-pim-1/antagonistas & inibidores , Inteligência Artificial , Cristalografia por Raios X , Desenho de Fármacos , Humanos , Ligantes , Modelos Moleculares , Estrutura Molecular , Conformação Proteica , Inibidores de Proteínas Quinases/química , Proteínas Proto-Oncogênicas c-pim-1/química , Relação Quantitativa Estrutura-Atividade
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