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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
4.
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.

5.
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
7.
Environ Health Perspect ; 129(4): 47013, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33929906

RESUMO

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.


Assuntos
Órgãos Governamentais , Animais , Simulação por Computador , Ratos , Testes de Toxicidade Aguda , Estados Unidos , United States Environmental Protection Agency
8.
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
9.
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.

10.
Toxicol Appl Pharmacol ; 407: 115244, 2020 11 15.
Artigo em Inglês | MEDLINE | ID: mdl-32961130

RESUMO

Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.3991561.


Assuntos
Bases de Dados Factuais , Receptores Citoplasmáticos e Nucleares/efeitos dos fármacos , Química Farmacêutica/métodos , Simulação por Computador , Coleta de Dados , Interpretação Estatística de Dados , Avaliação Pré-Clínica de Medicamentos , Humanos , Técnicas In Vitro , Modelos Moleculares , Bibliotecas de Moléculas Pequenas , Software , Toxicologia/métodos
11.
Molecules ; 25(13)2020 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-32605289

RESUMO

Chromatographic profiles of primary essential oils (EO) deliver valuable authentic information about composition and compound pattern. Primary EOs obtained from Pinus sylvestris L. (PS) from different global origins were analyzed using gas chromatography coupled to a flame ionization detector (GC-FID) and identified by GC hyphenated to mass spectrometer (GC-MS). A primary EO of PS was characterized by a distinct sesquiterpene pattern followed by a diterpene profile containing diterpenoids of the labdane, pimarane or abietane type. Based on their sesquiterpene compound patterns, primary EOs of PS were separated into their geographical origin using component analysis. Furthermore, differentiation of closely related pine EOs by partial least square discriminant analysis proved the existence of a primary EO of PS. The developed and validated PLS-DA model is suitable as a screening tool to assess the correct chemotaxonomic identification of a primary pine EOs as it classified all pine EOs correctly.


Assuntos
Óleos Voláteis/análise , Pinus sylvestris/química , Análise Discriminante , Diterpenos/análise , Diterpenos/química , Cromatografia Gasosa-Espectrometria de Massas , Estrutura Molecular , Óleos Vegetais/análise , Sesquiterpenos/análise , Sesquiterpenos/química
12.
Food Chem ; 315: 126248, 2020 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-32018076

RESUMO

Chianti is a precious red wine and enjoys a high reputation for its high quality in the world wine market. Despite this, the production region is small and product needs efficient tools to protect its brands and prevent adulterations. In this sense, ICP-MS combined with chemometrics has demonstrated its usefulness in food authentication. In this study, Chianti/Chianti Classico, authentic wines from vineyard of Toscana region (Italy), together samples from 18 different geographical regions, were analyzed with the objective of differentiate them from other Italian wines. Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to discriminate wine geographical origin. Rare Earth Elements (REE), major and trace elements all contributed to the discrimination of Chianti samples. General model was not suited to distinguish PDO red wines from samples, with similar chemical fingerprints, collected in some regions. Specific classification models enhanced the capability of discrimination, emphasizing the discriminant role of some elements.


Assuntos
Análise de Alimentos/métodos , Espectrometria de Massas/métodos , Vinho/análise , Análise Discriminante , Análise de Alimentos/estatística & dados numéricos , Itália , Análise dos Mínimos Quadrados , Limite de Detecção , Espectrometria de Massas/estatística & dados numéricos , Metais Terras Raras/análise , Oligoelementos/análise
13.
J Chem Inf Model ; 60(3): 1215-1223, 2020 03 23.
Artigo em Inglês | MEDLINE | ID: mdl-32073844

RESUMO

Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure-activity relationship (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale data sets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a data set of three properties (androgen receptor binding, agonism, and antagonism) for approximately 4000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches, majority voting and the Bayes consensus with discrete probability distributions, in both protective and nonprotective forms. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce the results of this study are available for download.


Assuntos
Relação Quantitativa Estrutura-Atividade , Teorema de Bayes , Consenso , Reprodutibilidade dos Testes
14.
Environ Health Perspect ; 128(2): 27002, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32074470

RESUMO

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.


Assuntos
Simulação por Computador , Disruptores Endócrinos , Androgênios , Bases de Dados Factuais , Ensaios de Triagem em Larga Escala , Humanos , Receptores Androgênicos , Estados Unidos , United States Environmental Protection Agency
15.
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
16.
Chimia (Aarau) ; 73(12): 1006-1011, 2019 Dec 18.
Artigo em Inglês | MEDLINE | ID: mdl-31883552

RESUMO

Drug discovery benefits from computational models aiding the identification of new chemical matter with bespoke properties. The field of de novo drug design has been particularly revitalized by adaptation of generative machine learning models from the field of natural language processing. These deep neural network models are trained on recognizing molecular structures and generate new molecular entities without relying on pre-determined sets of molecular building blocks and chemical transformations for virtual molecule construction. Implicit representation of chemical knowledge provides an alternative to formulating the molecular design task in terms of the established, explicit chemical vocabulary. Here, we review de novo molecular design approaches from the field of 'artificial intelligence', focusing on instances of deep generative models, and highlight the prospective application of long short-term memory models to hit and lead finding in medicinal chemistry.


Assuntos
Memória de Curto Prazo , Desenho de Fármacos , Aprendizado de Máquina , Redes Neurais de Computação , Estudos Prospectivos
17.
J Mol Model ; 25(5): 112, 2019 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-30953170

RESUMO

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.


Assuntos
Antineoplásicos/química , Modelos Moleculares , Neoplasias/tratamento farmacológico , Peptídeos/química , Células A549 , Antineoplásicos/uso terapêutico , Proliferação de Células/efeitos dos fármacos , Humanos , Células MCF-7 , Aprendizado de Máquina , Neoplasias/genética , Redes Neurais de Computação , Peptídeos/genética , Peptídeos/uso terapêutico
18.
ChemMedChem ; 14(12): 1129-1134, 2019 06 18.
Artigo em Inglês | MEDLINE | ID: mdl-30973672

RESUMO

A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (-)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models.


Assuntos
Doença de Alzheimer/tratamento farmacológico , Produtos Biológicos/farmacologia , Inibidores da Colinesterase/farmacologia , Desenho de Fármacos , Galantamina/farmacologia , Aprendizado de Máquina , Fármacos Neuroprotetores/farmacologia , Acetilcolinesterase/metabolismo , Doença de Alzheimer/metabolismo , Produtos Biológicos/síntese química , Produtos Biológicos/química , Linhagem Celular Tumoral , Inibidores da Colinesterase/síntese química , Inibidores da Colinesterase/química , Avaliação Pré-Clínica de Medicamentos , Galantamina/síntese química , Galantamina/química , Humanos , Ligantes , Simulação de Acoplamento Molecular , Estrutura Molecular , Fármacos Neuroprotetores/síntese química , Fármacos Neuroprotetores/química , Estereoisomerismo
19.
Integr Environ Assess Manag ; 15(3): 345-351, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-30821044

RESUMO

This paper concludes a special series of 7 articles (4 on toxicokinetic-toxicodynamic [TK-TD] models and 3 on quantitative structure-activity relationship [QSAR] models) published in previous issues of Integrated Environmental Assessment and Management (IEAM). The present paper summarizes the special series articles and highlights their contribution to the topic of increasing the regulatory applicability of effect models. For both TK-TD and QSAR approaches, we then describe the main research needs. The use of TK-TD models for describing sublethal effects must be better developed, particularly through the improvement of the dynamic energy budget (DEBtox) approach. The potential of TK-TD models for moving from lower (molecular) to higher (population) hierarchical levels is highlighted as a promising research line. Some relevant issues to improve the acceptance of QSAR models at the regulatory level are also described, such as increased transparency of the performance assessment and of the modeling algorithms, model documentation, relevance of the chosen target for regulatory needs, and improved mechanistic interpretability. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.


Assuntos
Ecotoxicologia , Poluentes Ambientais/toxicidade , Farmacocinética , Toxicocinética , Ecotoxicologia/legislação & jurisprudência , Ecotoxicologia/métodos , Poluentes Ambientais/farmacocinética , Poluentes Ambientais/farmacologia , Regulamentação Governamental , Modelos Biológicos , Relação Quantitativa Estrutura-Atividade
20.
J Chem Inf Model ; 59(5): 1839-1848, 2019 05 28.
Artigo em Inglês | MEDLINE | ID: mdl-30668916

RESUMO

The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes novel in silico models to identify organic AR modulators in the context of the Collaborative Modeling Project of Androgen Receptor Activity (CoMPARA), coordinated by the National Center of Computational Toxicology (U.S. Environmental Protection Agency). The collaborative project involved 35 international research groups to prioritize the experimental tests of approximatively 40k compounds, based on the predictions provided by each participant. In this paper, we describe our machine learning approach to predict the binding to AR, which is based on a consensus of a multivariate Bernoulli Naive Bayes, a Random Forest, and N-Nearest Neighbor classification models. The approach was developed in compliance with the Organization of Economic Cooperation and Development (OECD) principles, trained on 1687 ToxCast molecules classified according to 11 in vitro assays, and further validated on a set of 3,882 external compounds. The models provided robust and reliable predictions and were used to gather novel data-driven insights on the structural features related to AR binding, agonism, and antagonism.


Assuntos
Antagonistas de Receptores de Andrógenos/farmacologia , Androgênios/farmacologia , Disruptores Endócrinos/farmacologia , Aprendizado de Máquina , Receptores Androgênicos/metabolismo , Antagonistas de Receptores de Andrógenos/química , Androgênios/química , Descoberta de Drogas , Disruptores Endócrinos/química , Humanos , Simulação de Acoplamento Molecular , Ligação Proteica , Software
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