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RESEARCH PAPER · Drug Discovery Today Technologies · 2019

The good, the bad, and the ugly in chemical and biological data for machine learning

Tiago Rodrigues

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The good, the bad, and the ugly in chemical and biological data for machine learningOriginalEditorial for the Special Section "Artificial Intelligence in Drug Discovery"0Machine learning for metabolic engineering: A review313Problems that Can Occur when Assaying Extracts to Pure Compounds in Biological Systems13Taking the leap between analytical chemistry and artificial intelligence: A tutorial review175Critical assessment of AI in drug discovery106Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules4Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization20Facts and Figures on Materials Science and Nanotechnology Progress and Investment103Combating small-molecule aggregation with machine learning21Nuisance small molecules under a machine-learning lens3Machine Learning for Chemical Reactivity: The Importance of Failed Experiments217Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente8Evaluation guidelines for machine learning tools in the chemical sciences180Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond32A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives7A focus on the use of real-world datasets for yield prediction3A Tuning Approach Using Genetic Algorithms for Emergency Incidents Classification in Social Media in Ecuador1The rise of automated curiosity-driven discoveries in chemistry20Explainable uncertainty quantifications for deep learning-based molecular property prediction84Reaction Impurity Prediction using a Data Mining Approach**9Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada7Automatic acoustic recognition of pollinating bee species can be highly improved by Deep Learning models accompanied by pre-training and strong data augmentation25Functional Group Evaluation Kit for Digitalization of Information on the Functional Group Compatibility and Chemoselectivity of Organic Reactions32Machine Learning for the Optimization of Chemical Reaction Conditions6Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions51Limitations of representation learning in small molecule property prediction17Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside73Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction33The Influence of Artificial Intelligence in Drug Discovery and Development0Potential anti‐obesity effect of saponin metabolites from adzuki beans: A computational approach12Retrosynthesis prediction enhanced by in-silico reaction data augmentation3KNIME workflows for applications in medicinal and computational chemistry11Lessons learnt from machine learning in early stages of drug discovery5Best practices for machine learning in antibody discovery and development24Predicting Emission Wavelengths in Benzobisoxazole-Based OLEDs with Gradient Boosted Ensemble Models3Bridging Machine Learning and Thermodynamics for Accurate p K a Prediction37Navigating the archaeal frontier: insights and projections from bioinformatic pipelines8Artificial intelligence and machine learning at various stages and scales of process systems engineering36Data Quality as a Bottleneck in AI-Driven Natural Product Discovery: Bias, Completeness, Assay Reliability, Chemical Annotation, and Model Generalizability0Chiral Intelligence: The Artificial Intelligence‐Driven Future of Chiroptical Properties8Machine learning models for prediction of CO2 adsorption capacity of metal-organic frameworks6Harnessing Synergies between Combinatorial Microfluidics and Machine Learning for Chemistry, Biology, and Fluidic Design6Adaptive sampling methods facilitate the determination of reliable dataset sizes for evidence-based modeling5Accelerating discoveries in cancer nanomedicine using AI16DNA-encoded libraries through a machine learning lens: challenges and opportunities0Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]‐helicene Family2Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation7Assessing biological activities of polyphenols in the context of 4R – challenges of a ubiquitous compound class with complex modes of action3AI-driven cellular immunotherapy: transforming CAR-T engineering and translation2Human organ-on-a-chip technology as a catalyst for drug discovery0AVALIAÇÃO DO DESEMPENHO DE MODELOS DE APRENDIZADO DE MÁQUINA UTILIZANDO DADOS SINTÉTICOS: UM ESTUDO EXPERIMENTAL BASEADO NA LITERATURA1A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer0

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  1. Editorial for the Special Section "Artificial Intelligence in Drug Discovery"

    Drug Discovery Today Technologies · 2019-12-01

    Johannes Kirchmair

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  2. Machine learning for metabolic engineering: A review

    Metabolic Engineering · 2020-11-19

    Christopher E. Lawson, Jose Manuel Martí, Tijana Radivojević, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake Alexander Simmons, Christopher J. Petzold, Steven W. Singer, Aindrila Mukhopadhyay, Deepti Tanjore, Joshua G. Dunn, Héctor García Martín

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  3. Problems that Can Occur when Assaying Extracts to Pure Compounds in Biological Systems

    Current Therapeutic Research · 2021-01-01

    David J. Newman

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  4. Taking the leap between analytical chemistry and artificial intelligence: A tutorial review

    Analytica Chimica Acta · 2021-03-15

    Lucas B. Ayres, Federico J.V. Gómez, Jeb R. Linton, María Fernanda Silva, Carlos D. García

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  5. Critical assessment of AI in drug discovery

    Expert Opinion on Drug Discovery · 2021-04-19

    W. Patrick Walters, Regina Barzilay

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  6. Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules

    Frontiers in Chemistry · 2021-05-04

    Alastair D. G. Lawson, Malcolm MacCoss, Dominique Baeten, Alex Macpherson, Jiye Shi, Alistair J. Henry

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  7. Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization

    The Journal of Organic Chemistry · 2021-07-08

    A. Filipa Almeida, Filipe Ataíde, Rui M. S. Loureiro, Rui Moreira, Tiago Rodrigues

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  8. Facts and Figures on Materials Science and Nanotechnology Progress and Investment

    ACS Nano · 2021-07-29

    Sepehr Talebian, Tiago Rodrigues, José das Neves, Bruno Filipe Carmelino Cardoso Sarmento, Róbert Langer, João Conde

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  9. Combating small-molecule aggregation with machine learning

    Cell Reports Physical Science · 2021-09-01

    Kuan Lee, Ann Yang, Yen‐Chu Lin, Daniel Reker, Gonçalo J. L. Bernardes, Tiago Rodrigues

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  10. Nuisance small molecules under a machine-learning lens

    Digital Discovery · 2022-01-01

    Tiago Rodrigues

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  11. Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

    Angewandte Chemie International Edition · 2022-05-05

    Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius

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  12. Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente

    Angewandte Chemie · 2022-05-05

    Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius

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  13. Evaluation guidelines for machine learning tools in the chemical sciences

    Nature Reviews Chemistry · 2022-05-24

    Andreas Bender, Nadine Schneider, Marwin Segler, W. Patrick Walters, Ola Engkvist, Tiago Rodrigues

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  14. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

    The Annual Review of Pharmacology and Toxicology · 2022-06-09

    Douglas McNair

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  15. A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives

    Communications Chemistry · 2022-11-22

    Raku Shirasawa, Ichiro Takemura, Shinnosuke Hattori, Yuuya Nagata

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  16. A focus on the use of real-world datasets for yield prediction

    Chemical Science · 2023-01-01

    Latimah Bustillo, Tiago Rodrigues

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  17. A Tuning Approach Using Genetic Algorithms for Emergency Incidents Classification in Social Media in Ecuador

    Communications in computer and information science · 2023-01-01

    Joel Garcia-Arteaga, Jesús Zambrano-Zambrano, Jorge Antonio Párraga Álava, Jorge Rodas‐Silva

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  18. The rise of automated curiosity-driven discoveries in chemistry

    Chemical Science · 2023-01-01

    Latimah Bustillo, Teodoro Laino, Tiago Rodrigues

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  19. Explainable uncertainty quantifications for deep learning-based molecular property prediction

    Journal of Cheminformatics · 2023-02-03

    Chu-I Yang, Yi‐Pei Li

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  20. Reaction Impurity Prediction using a Data Mining Approach**

    Chemistry - Methods · 2023-03-27

    Adarsh Arun, Zhen Guo, Simon Sung, Alexei A. Lapkin

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  21. Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada

    Educación Química · 2023-04-11

    Fernanda I. Saldívar‐González, Eli Fernández‐de Gortari, José L. Medina‐Franco

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  22. Automatic acoustic recognition of pollinating bee species can be highly improved by Deep Learning models accompanied by pre-training and strong data augmentation

    Frontiers in Plant Science · 2023-04-14

    Alef Iury Siqueira Ferreira, Nádia Félix Felipe da Silva, Fernanda Neiva Mesquita, Thierson Couto Rosa, Víctor Hugo Monzón, José Neiva Mesquita Neto

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  23. Functional Group Evaluation Kit for Digitalization of Information on the Functional Group Compatibility and Chemoselectivity of Organic Reactions

    Bulletin of the Chemical Society of Japan · 2023-04-18

    Natsuki Saito, Anna Nawachi, Yuta Kondo, Jeesoo Choi, Hiroyuki Morimoto, Takashi Ohshima

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  24. Machine Learning for the Optimization of Chemical Reaction Conditions

    Unknown journal · 2023-08-31

    A. Filipa Almeida, Tiago Rodrigues

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  25. Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions

    The Journal of Physical Chemistry A · 2023-09-28

    Shilpa Shilpa, Gargee Kashyap, Raghavan B. Sunoj

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  26. Limitations of representation learning in small molecule property prediction

    Nature Communications · 2023-10-13

    Ana Laura Dias, Latimah Bustillo, Tiago Rodrigues

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  27. Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside

    Cureus · 2023-10-22

    K S Vidhya, Ayesha Sultana, Naveen Kumar M, Harish Rangareddy

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  28. Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

    The Journal of Physical Chemistry B · 2023-11-28

    Karol Baran, Adam Kloskowski

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  29. The Influence of Artificial Intelligence in Drug Discovery and Development

    Medical virology · 2024-01-01

    Sushil Krishnan, Rajendran K, V. N. Yogeshwaran, B. Radhakrishnan

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  30. Potential anti‐obesity effect of saponin metabolites from adzuki beans: A computational approach

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    Ashaimaa Yehia Moussa, Abdullah R. Alanzi, Jinhai Luo, Sookja Kim Chung, Baojun Xu

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  31. Retrosynthesis prediction enhanced by in-silico reaction data augmentation

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  32. KNIME workflows for applications in medicinal and computational chemistry

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    Ruchira Joshi, Zipeng Zheng, Palak Agarwal, Ma’mon M. Hatmal, Xinmin Chang, Paul M. Seidler, Ian S. Haworth

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  33. Lessons learnt from machine learning in early stages of drug discovery

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  34. Best practices for machine learning in antibody discovery and development

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  35. Predicting Emission Wavelengths in Benzobisoxazole-Based OLEDs with Gradient Boosted Ensemble Models

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  36. Bridging Machine Learning and Thermodynamics for Accurate p K a Prediction

    JACS Au · 2024-07-17

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  37. Navigating the archaeal frontier: insights and projections from bioinformatic pipelines

    Frontiers in Microbiology · 2024-09-23

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  38. Artificial intelligence and machine learning at various stages and scales of process systems engineering

    The Canadian Journal of Chemical Engineering · 2024-11-06

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  39. Data Quality as a Bottleneck in AI-Driven Natural Product Discovery: Bias, Completeness, Assay Reliability, Chemical Annotation, and Model Generalizability

    International Journal of Pharmaceutical and Phytopharmacological Research · 2025-01-01

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  40. Chiral Intelligence: The Artificial Intelligence‐Driven Future of Chiroptical Properties

    ChemPhotoChem · 2025-05-01

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  41. Machine learning models for prediction of CO2 adsorption capacity of metal-organic frameworks

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  42. Harnessing Synergies between Combinatorial Microfluidics and Machine Learning for Chemistry, Biology, and Fluidic Design

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  43. Adaptive sampling methods facilitate the determination of reliable dataset sizes for evidence-based modeling

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  44. Accelerating discoveries in cancer nanomedicine using AI

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  45. DNA-encoded libraries through a machine learning lens: challenges and opportunities

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  46. Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]‐helicene Family

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  47. Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation

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  48. Assessing biological activities of polyphenols in the context of 4R – challenges of a ubiquitous compound class with complex modes of action

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  49. AI-driven cellular immunotherapy: transforming CAR-T engineering and translation

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  50. Human organ-on-a-chip technology as a catalyst for drug discovery

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