Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5364_Библиотеки_им_академика_М_И_Перельмана.pdf
152 P. O. Fernandes and V. G. Maltarollo
A virtual screening comprising multiple computational methods was reported by
Mushtaq and colleagues [124] to identify compounds with anti-interleukin-2 activity. The authors validated a molecular docking protocol and applied it in combination with pharmacophore filtering starting from a library containing 11.9 million
compounds from ZINC and resulting in 24 compounds that had their activities
predicted with a properly validated CoMFA model. Then, only nine compounds
were submitted to experimental validation and three of them were considered
promising hits due to the IL-2 inhibitory effect.
An interesting application of machine-learning-based virtual screening was
reported by Barbosa et al. [125] In this work, they generated and validated kNN
and Random Forest models to predi ct activity against Trypanosoma cruzi and used
the models to screen a natural products database. After the selection of a virtual hit,
they identified a plant that produced this hit as a metabolite (Cymbopogon
schoenanthus), performed the isolation and identification of the selected compound,
a diterpenoid called andrographolide, and tested it. As a result of the experimental
validation, this compound showed IC
values of 29.4 and 2.9 μM against
50
trypomastigote and amastigote forms of T. cruzi and a selectivity index equal to
32, comparable with the positive control.
In 2024, Fernandes and colleagues reported a machine learning-based virtual
screening [126] or discovering anti bacterial compounds against methicillinsusceptible and resistant strains of Staphylococcus aureus. In this work, they generated descriptor-based QSAR classification models using several diverse machine
learning methods for three data sets : one comprised of compounds with activity
against susceptible strains of S. aureus; another one comprised of compounds with
activity against resistant strains; and a third one comprised of compounds with
activity against both strains. This last data set was very important to weigh the
consensus selection of hits for experiment al testing since it comprised both modeled
activities. In this sense, the hit rate of models generated from this data set was higher
than the other two models.
Wong et al. [127] experimentally screened 39,312 compounds against a
methicillin-susceptible S. aureus strain (RN4220) as a model for antibacter ial
activity and against human liver carcinoma cells (HepG2), human primary skeletal
muscle cells (HSkMCs), and human lung fibroblast cells (IMR-90) as models for
cytotoxicity. After, they used Chemprop to train a graph neural network to predict a
binary classification task for all properties (activity and toxicities). After training and
validation of models, the authors screened two libraries for obtaining compounds
with predicted antibacterial activity and no cytotoxic profile. Following this, potential false-positive compounds were removed using PAINS rules and undesired
compounds due to reactivity, metabolic instability, and generalized toxicity were
also removed by using Brenk structural alerts. Lastly, the authors selected compounds with similarity scores equal or lower than 0.5 in comparison with data set
compounds. In this sense, they started with approximately 12 million compounds,
and, after filtering, they selected 1261 compounds. As a strategy to interpreting the
models, the authors used Monte Carlo tree searches to explore the chemical space
and understand the smallest portion of a molecule responsible for their classification
6 QSAR and Machine Learning Predictors 153
as active. With this approach, it was possible to highlight important structural
features responsible for predictions. Then, using the rational structural predictions
they filtered the hits according to analogs that match known antibacterial classes
such as quinolone, cephalosporins, and β-lactams, then selecting 9 compounds for
experimental validations. Four of the nine tested compounds were actives reaching a
44% success rate.
This last example is not an actual application of QSAR for drug design discovery,
but a free platform available for early stages ADME profile prediction [128]. The
Pharmacokinetics Profiler (PhaKinPro, available at: https://phakinpro .mml.unc.edu/
) is a web server with QSAR models to predict hepatic stability, microsomal half-life
in sub-cellular and tissue, renal clearance, blood–brain barrier (BBB) permeability,
central nervous system (CNS) activity, Caco-2 permeability, plasma protein binding,
plasma half-life, microsomal intrinsic clearance, and oral bioavailability. Despite the
validation metrics of each model, the predictions at the web server provide the
confidence of prediction, if the compound is inside of AD or not, and the importance
of molecular fragments for the predictio n as the interpretation of the model.
8 Challenges and Perspectives
As mentioned, statistical methods and algorithms have a poor ability to handle data
from different sources and, consecutively, biological data from different protocols.
In that sense, ML methods emerge as promising methods to model data with noise
due to the superior ability of generalization. Indeed, this task has naturally evolved
since biology and chemistry joined the big data concept with large databases.
However, it desired methods to ensure how the data noise affects the quality of the
predictions.
Another related issue is better attention and report of imbalanced data sets for
classification models and potential gaps in modeled activities for regression models.
Very often, authors underreport this aspect of data sets and, of course, biased data
sets produce biased models which make biased predictions. The balance between
classes introduces a bias in the modeled activity. However, other sources of bias
must be avoided and well reported in QSAR modeling protocols, such as structural
and physicochemical biases which could be solved (or, at least, used to warn
potential users of reported models) with the combination of data set characterization
and proper AD definition.
Multi-task models, in other words, models with the ability to predict more than
one property ( y) are well established in the literature; however, authors very often
report parallel individual models. Maybe, the lack of ready-to-use software with this
ability and the need for coding limit the spreading of this modality of modeling. In
the same way, transfer learning is a set of methods that transfer knowledge from one
trained and validated model to another. Of course, both models must share mechanisms or similarities in the modeled property, for example, models to predict the
binding affinity of ligands to close homolog and structurally similar enzymes. This
154 P. O. Fernandes and V. G. Maltarollo
concept of transfer learning is extremely useful since it saves enormous amounts of
time in training steps for similar task models. But, as multi-task models, transfer
learning in QSAR is underreported in comparison to regular standard QSAR models.
Finally, not only the QSAR field, but all subjects across chemistry should feel the
impact of large language models (LLM) because now they have been closing the gap
between machines and humans across many knowledge domains [129] and its
potential already has been demonstrated to understand complex molecular distribution [130]. The recent interest of the worldwide community on language models
such as Generative Pre-trained Transformer 3 (GPT-3) from OpenAI popularized
with the ChatGPT API has shed light on the subject. In this sense, LLM has been
explored to predict chemical tasks as well as is in the spotlight of novel methods to
be explored.
Acknowledgements The authors would like to thank the Fundação de Amparo à Pesquisa do
Estado de Minas Gerais - FAPEMIG (grants APQ-01818-21 and RED-00110-23).
References
1. Cros, A. (1863). Action de l’alcohol Amylique Sur l’organisme (PhD Thesis, Thesis). Strasbourg, University of Strasbourg.
2. Mills, E. J. (1884). XXIII. On melting-point and boiling-point as related to chemical composition. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science,
17(105), 173–187.
3. Hansch, C., Maloney, P. P., Fujita, T., & Muir, R. M. (1962). Correlation of biological activity
of phenoxyacetic acids with Hammett substituent constants and partition coef ficients. Nature,
194(4824), 178–180.
4. Hansch, C., Muir, R. M., Fujita, T., Maloney, P. P., Geiger, F., & Streich, M. (1963). The
correlation of biological Activity of plant growth regulators and Chloromycetin derivatives
with Hammett constants and partition coefficients. Journal of the American Chemical Society,
85(18), 2817–2824.
5. Fujita, T., Iwasa, J., & Hansch, C. (1964). A new substituent constant, π, derived from partition
coefficients. Journal of the American Chemical Society, 86(23), 5175–5180.
6. Soares, T. A., Nunes-Alves, A., Mazzolari, A., Ruggiu, F., Wei, G.-W., & Merz, K. (2022).
The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled
by the surge of machine learning methods. Journal of Chemical Information and Modeling,
62(22), 5317–5320.
7. Shi, Y., Yu, M., Liu, J., Yan, F., Luo, Z.-H., & Zhou, Y.-N. (2022). Quantitative structure–
property relationship model for predicting the propagation rate coefficient in free-radical
polymerization. Macromolecules, 55(21), 9397–9410.
8. Yu, M., Shi, Y., Liu, X., Jia, Q., Wang, Q., Luo, Z.-H., Yan, F., & Zhou, Y.-N. (2023).
Quantitative Structure-Property Relationship (QSPR) framework assists in rapid mining of
highly thermostable polyimides. Chemical Engineering Journal, 465, 142768.
9. Czub, N., Szlęk, J., Pacławski, A., Klimończyk, K., Puccetti, M., & Mendyk, A. (2023).
Artificial intelligence-based quantitative structure– property relationship model for predicting
human intestinal absorption of compounds with serotonergic activity. Molecular
Pharmaceutics, 20(5), 2545–2555.
6 QSAR and Machine Learning Predictors 155
10. Sterling, A. J., Zavitsanou, S., Ford, J., & Duarte, F. (2021). Selectivity in organocatalysis—
From qualitative to quantitative predictive models. WIREs Computational Molecular Science,
11(5), e1518.
11. Eckhoff, M., Diedrich, J. V., Mücke, M., & Proppe, J. (2024). Quantitative structure–reactivity
relationships for synthesis planning: The benzhydrylium case. The Journal of Physical
Chemistry. A, 128(1), 343–354.
12. Paradies, J. (2023). Structure-reactivity relationships in borane-based FLP-catalyzed hydrogenations, dehydrogenations, and cycloisomerizations. Accounts of Chemical Research, 56(7),
821–834.
13. Wang, L.-L., Ding, J.-J., Pan, L., Fu, L., Tian, J.-H., Cao, D.-S., Jiang, H., & Ding, X.-Q.
(2021). Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice. Journal of Hazardous Materials,
401, 123724.
14. Mukherjee, R. K., Kumar, V., & Roy, K. (2022). Ecotoxicological QSTR and QSTTR
modeling for the prediction of acute oral toxicity of pesticides against multiple avian species.
Environmental Science & Technology, 56(1), 335–348.
15. Rai, M., Paudel, N., Sakhrie, M., Gemmati, D., Khan, I. A., Tisato, V., Kanase, A., Schulz, A.,
& Singh, A. V. (2023). Perspective on quantitative structure–toxicity relationship (QSTR)
models to predict hepatic biotransformation of xenobiotics. Liver, 3(3), 448–462.
16. Fourches, D., Muratov, E., & Tropsha, A. (2010). Trust, but Verify: On the importance of
chemical Structure curation in cheminformatics and QSAR modeling research. Journal of
Chemical Information and Modeling, 50(7), 1189–1204.
17. Fourches, D., Muratov, E., & Tropsha, A. (2016). Trust, but Verify II: A practical guide to
chemogenomics data curation. Journal of Chemical Information and Modeling, 56(7),
1243–1252.
18. McGibbon, M., Shave, S., Dong, J., Gao, Y., Houston, D. R., Xie, J., Yang, Y., Schwaller, P.,
& Blay, V. (2024). From intuition to AI: Evolution of small molecule representations in drug
discovery. Briefings in Bioinformatics, 25(1), bbad422.
19. Khan, A. U. (2016). Descriptors and their selection methods in QSAR analysis: Paradigm for
drug design. Drug Discovery Today, 21(8), 1291–1302.
20. Cereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcia-Vallvé, S., & Pujadas,
G. (2015). Molecular fingerprint similarity search in virtual screening. Methods, 71,58–63.
21. Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design - A review.
Current Topics in Medicinal Chemistry, 10(1), 95–115.
22. G. Damale, M., N. Harke, S., A. Kalam Khan, F., B. Shinde, D., & N. Sangshetti, J. (2014)
Recent advances in multidimensional QSAR (4D-6D): A critical review. Mini Reviews in
Medicinal Chemistry, 14(1), 35–55.
23. Polanski, J. (2009). Receptor dependent multidimensional QSAR for modeling drug-receptor
interactions. Current Medicinal Chemistry, 16(25), 3243–3257.
24. Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation.
Molecular Informatics, 29(6–7), 476
25. Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P.,
Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., Davies, M., Dedman, N., Karlsson, A.,
Magariños, M. P., Overington, J. P., Papadatos, G., Smit, I., & Leach, A. R. (2017). The
ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945–D954.
26. Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., Zhou, Z., Han, L., Karapetyan, K.,
Dracheva, S., Shoemaker, B. A., Bolton, E., Gindulyte, A., & Bryant, S. H. (2012).
PubChem’s BioAssay database. Nucleic Acids Research, 40(D1), D400–D412.
27. Organisation for Economic Co-operation and Development. OECD principles for the valida-
tion, for regulatory purposes, of (Quantitative) structure-activity relationship models. https://
www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf. Accessed 2021-12-07.
28. Di Paolo, T. (1978). Structure-activity relationships of anesthetic ethers using molecular
connectivity. Journal of Pharmaceutical Sciences, 67(4), 564–566.
–488.
156 P. O. Fernandes and V. G. Maltarollo
29. Hansch, C., & Klein, T. E. (1986). Molecular graphics and QSAR in the study of enzymeligand interactions. On the definition of bioreceptors. Accounts of Chemical Research, 19(12),
392–400.
30. Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988). Comparative Molecular Field Analysis
(CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American
Chemical Society, 110(18), 5959–5967.
31. Clark, M., Cramer, R. D., Jones, D. M., Patterson, D. E., & Simeroth, P. E. (1990). Comparative Molecular Field Analysis (CoMFA). 2. Toward its use with 3D-structural databases.
Tetrahedron Computer Methodology, 3(1), 47–59.
32. Klebe, G., Abraham, U., & Mietzner, T. (1994). Molecular similarity indices in a comparative
analysis (CoMSIA) of drug molecules to correlate and predict their biological Activity.
Journal of Medicinal Chemistry, 37(24), 4130–4146.
33. Lowis, D. R. (1997). HQSAR: A new, highly predictive QSAR technique. Tripos Technical
Notes, 1(5), 17.
34. Seel, M., Turner, D. B., & Willett, P. (1999). Effect of parameter variations on the effectiveness of HQSAR analyses. Quantitative Structure-Activity Relationships, 18(3), 245–252.
35. Abdizadeh, R., Hadizadeh, F., & Abdizadeh, T. (2020). QSAR analysis of Coumarin-based
Benzamides as histone deacetylase inhibitors using CoMFA, CoMSIA and HQSAR methods.
Journal of Molecular Structure, 1199, 126961.
36. Ding, H., Xing, F., Zou, L., & Zhao, L. (2024). QSAR analysis of VEGFR-2 inhibitors based
on machine learning, Topomer CoMFA and molecule docking. BMC Chemistry, 18(1), 59.
37. Edache, E. I., Uzairu, A., Mamza, P. A., Shallangwa, G. A., & Ibrahim, M. T. (2024). Design
of some potent non-toxic autoimmune disorder inhibitors Based on 2D-QSAR, CoMFA,
molecular docking, and molecular dynamics investigations. Intelligent Pharmacy.
38. Abdizadeh, R., Hadizadeh, F., & Abdizadeh, T. (2020). Molecular modeling studies of antiAlzheimer agents by QSAR, molecular docking and molecular dynamics simulations techniques. Medicinal Chemistry, 16(7), 903–927.
39. Lino, C. I., Gonçalves de Souza, I., Borelli, B. M., Silvério Matos, T. T., Santos Teixeira, I. N.,
Ramos, J. P., Maria de Souza Fagundes, E., de Oliveira Fernandes, P., Maltarollo, V. G.,
Johann, S., & de Oliveira, R. B. (2018). Synthesis, molecular modeling studies and evaluation
of antifungal activity of a novel series of Thiazole derivatives. European Journal of Medicinal
Chemistry, 151, 248–260.
40. Veríssimo, G. C., Menezes Dutra, E. F., Teotonio Dias, A. L., de Oliveira Fernandes, P.,
Kronenberger, T., Gomes, M. A., & Maltarollo, V. G. (2019). HQSAR and random forestbased QSAR models for Anti-T. Vaginalis activities of nitroimidazoles derivatives. Journal of
Molecular Graphics and Modelling, 90, 180–191.
41. Wu, Z., Zhu, M., Kang, Y., Leung, E. L.-H., Lei, T., Shen, C., Jiang, D., Wang, Z., Cao, D., &
Hou, T. (2021). Do we need different machine learning algorithms for QSAR modeling? A
comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Briefings
in Bioinformatics, 22(4), bbaa321.
42. Brown, F. K., Sherer, E. C., Johnson, S. A., Holloway, M. K., & Sherborne, B. S. (2017). The
evolution of drug design at Merck Research Laboratories. Journal of Computer-Aided Molec-
ular Design, 31(3), 255–266.
43. Loyola-Gonzalez, O. (2019). Black-box vs White-box: Understanding their advantages and
weaknesses from a practical point of view. IEEE Access, 7, 154096–154113.
44. Barber, C., Heghes, C., & Johnston, L. (2024). A framework to support the application of the
OECD Guidance documents on (Q)SAR model validation and prediction assessment for
regulatory decisions. Computational Toxicology, 30, 100305.
45. Organisation for Economic Co-operation and Development. (2007). Guidance document on
the validation of (quantitative) structure-activity relationship [(Q) SAR] models. Organisation
for Economic Co-operation and Development.
46. Cronin, M. T. D., & Schultz, T. W. (2003). Pitfalls in QSAR. Journal of Molecular Structure:
THEOCHEM, 622(1), 39–51.
6 QSAR and Machine Learning Predictors 157
47. Borota, A., Mracec, M., Gruia, A., Rad-Curpăn, R., Ostopovici-Halip, L., & Mracec,
M. (2011). A QSAR study using MTD method and dragon descriptors for a series of selective
ligands of α2C adrenoceptor. European Journal of Medicinal Chemistry, 46(3), 877– 884.
48. Rodríguez-Pérez, R., & Bajorath, J. (2021). Explainable machine learning for property predictions in compound optimization. Journal of Medicinal Chemistry, 64(24), 17744–17752.
49. Jouan-Rimbaud, D., Massart, D. L., & De Noord, O. E. (1996). Random correlation in variable
selection for multivariate calibration with a genetic algorithm. Chemometrics and Intelligent
Laboratory Systems, 35(2), 213–220.
50. Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and
Computer Sciences, 44(1), 1–12.
51. Topliss, J. G., & Edwards, R. P. (1979). Chance factors in studies of Quantitative StructureActivity Relationships. Journal of Medicinal Chemistry, 22(10), 1238–1244.
52. Wold, S., & Dunn, W. J. (1983). Multivariate Quantitative Structure-Activity Relationships
(QSAR): Conditions for their applicability. Journal of Chemical Information and Computer
Sciences, 23(1), 6–13.
53. Clark, M., & Cramer, R. D., III. (1993). The probability of chance correlation using partial
least squares (PLS). Quantitative Structure-Activity Relationships, 12 (2), 137–145.
54. Schaper, K.-J., Kunz, B., & Raevsky, O. A. (2003). Analysis of water solubility data on the
basis of HYBOT descriptors. QSAR & Combinatorial Science, 22(9–10), 943–958.
55. Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P.,
Mosquera, J. F., Mutowo, P., Nowotka, M., Gordillo-Marañón, M., Hunter, F., Junco, L.,
Mugumbate, G., Rodriguez-Lopez, M., Atkinson, F., Bosc, N., Radoux, C. J., Segura-Cabrera,
A., Hersey, A., & Leach, A. R. (2019). ChEMBL: Towards direct deposition of bioassay data.
Nucleic Acids Research, 47(D1), D930–D940.
56. Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen,
P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2023). PubChem 2023 update. Nucleic
Acids Research, 51(D1), D1373–D1380.
57. Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., & Chong, J. (2016). BindingDB in
2015: A public database for medicinal chemistry, computational chemistry and systems
pharmacology. Nucleic Acids Research, 44(D1), D1045–D1053.
58. Mauri, A., Consonni, V., Pavan, M., Todeschini, R., et al. (2006). Dragon software: An easy
approach to molecular descriptor calculations. Match, 56(2), 237–248.
59. Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., & Willighagen, E. (2003). The
Chemistry Development Kit (CDK): An open-source Java library for chemo- and bioinformatics. Journal of Chemical Information and Computer Sciences, 43(2), 493–500.
60. Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V. A.,
Radchenko, E. V., Zefirov, N. S., Makarenko, A. S., Tanchuk, V. Y., & Prokopenko, V. V.
(2005). Virtual computational chemistry laboratory—Design and description. Journal of
Computer-Aided Molecular Design, 19 (6), 453
61. Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L., Su, Z., Perkins, R., & Tong, W. (2008).
Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics.
Journal of Chemical Information and Modeling, 48(7), 1337–1344.
62. O’Boyle, N. M., Morley, C., & Hutchison, G. R. (2008). Pybel: A Ppython wrapper for the
OpenBabel cheminformatics toolkit. Chemistry Central Journal, 2(1), 5.
63. Yap, C. W. (2011). PaDEL-descriptor: An open source software to calculate molecular
descriptors and fingerprints. Journal of Computational Chemistry, 32(7), 1466–1474.
64. Cao, D.-S., Liang, Y.-Z., Yan, J., Tan, G.-S., Xu, Q.-S., & Liu, S. (2013). PyDPI: Freely
available python package for chemoinformatics, bioinformatics, and chemogenomics studies.
Journal of Chemical Information and Modeling, 53(11), 3086–3096.
65. Cao, D.-S., Xu, Q.-S., Hu, Q.-N., & Liang, Y.-Z. (2013). ChemoPy: Freely available Python
package for computational biology and chemoinformatics. Bioinformatics, 29(8), 1092–1094.
–463.
158 P. O. Fernandes and V. G. Maltarollo
66. Dong, J., Cao, D.-S., Miao, H.-Y., Liu, S., Deng, B.-C., Yun, Y.-H., Wang, N.-N., Lu, A.-P.,
Zeng, W.-B., & Chen, A. F. (2015). ChemDes: An integrated web-based platform for
molecular descriptor and fingerprint computation. Journal of Cheminformatics, 7(1), 60.
67. Cao, D.-S., Xiao, N., Xu, Q.-S., & Chen, A. F. (2015). Rcpi: R/Bioconductor package to
generate various descriptors of proteins, compounds and their interactionsc. Bioinformatics,
31(2), 279–281.
68. Dong, J., Yao, Z.-J., Wen, M., Zhu, M.-F., Wang, N.-N., Miao, H.-Y., Lu, A.-P., Zeng, W.-B.,
& Cao, D.-S. (2016). BioTriangle: A web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions. Journal of
Cheminformatics, 8(1), 34.
69. Dong, J., Yao, Z.-J., Zhu, M.-F., Wang, N.-N., Lu, B., Chen, A. F., Lu, A.-P., Miao, H., Zeng,
W.-B., & Cao, D.-S. (2017). ChemSAR: An online pipelining platform for molecular SAR
modeling. Journal of Cheminformatics, 9(1), 27.
70. Moriwaki, H., Tian, Y.-S., Kawashita, N., & Takagi, T. (2018). Mordred: A molecular
descriptor calculator. Journal of Cheminformatics, 10(1), 4.
71. Dong, J., Yao, Z.-J., Zhang, L., Luo, F., Lin, Q., Lu, A.-P., Chen, A. F., & Cao, D.-S. (2018).
PyBioMed: A python library for various molecular representations of chemicals, proteins and
DNAs and their interactions. Journal of Cheminformatics, 10(1), 16.
72. Mauri, A. (2020). alvaDesc: A tool to calculate and analyze molecular descriptors and
fingerprints. In K. Roy (Ed.), Ecotoxicological QSARs (pp. 801–820). Springer US.
73. Dong, J., Zhu, M.-F., Yun, Y.-H., Lu, A.-P., Hou, T.-J., & Cao, D.-S. (2021). BioMedR: An
R/CRAN package for integrated data analysis pipeline in biomedical study. Briefings in
Bioinformatics, 22(1), 474–484.
74. Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017).
Convolutional embedding of attributed molecular graphs for physical property prediction.
Journal of Chemical Information and Modeling, 57(8), 1757–1772.
75. Wang, Y., Wu, S., Duan, Y., & Huang, Y. (2022). A point cloud-based deep learning strategy
for protein–ligand binding affinity prediction. Briefings in Bioinformatics, 23(1), bbab474.
76. David, L., Thakkar, A., Mercado, R., & Engkvist, O. (2020). Molecular representations in
AI-driven drug discovery: A review and practical guide. Journal of Cheminformatics, 12(1),
56.
77. Heid, E., Greenman, K. P., Chung, Y., Li, S.-C., Graff, D. E., Vermeire, F. H., Wu, H., Green,
W. H., & McGill, C. J. (2024). Chemprop: A machine learning package for chemical property
prediction. Journal of Chemical Information and Modeling, 64(1), 9–17.
78. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair,
C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020). A deep learning
approach to antibiotic discovery. Cell, 180(4), 688–702.
79. Jin, W., Stokes, J. M., Eastman, R. T., Itkin, Z., Zakharov, A. V., Collins, J. J., Jaakkola, T. S.,
& Barzilay, R. (2021). Deep learning identifies synergistic drug combinations for treating
COVID-19. Proceedings of the National Academy of Sciences, 118(39), e2105070118.
80. Lim, M. A., Yang, S., Mai, H., & Cheng, A. C. (2022). Exploring deep learning of quantum
chemical properties for absorption, distribution, metabolism, and excretion predictions. Jour-
nal of Chemical Information and Modeling, 62(24), 6336–6341.
81. Lenselink, E. B., & Stouten, P. F. W. (2021). Multitask machine learning models for predicting
Lipophilicity (logP) in the SAMPL7 challenge. Journal of Computer-Aided Molecular
Design, 35(8), 901–
82. McGill, C., Forsuelo, M., Guan, Y., & Green, W. H. (2021). Predicting infrared spectra with
message passing neural networks. Journal of Chemical Information and Modeling, 61(6),
2594–2609.
83. Masand, V. H., Mahajan, D. T., Nazeruddin, G. M., Hadda, T. B., Rastija, V., & Alfeefy,
A. M. (2015). Effect of information leakage and method of splitting (rational and random) on
external predictive ability and behavior of different statistical parameters of QSAR model.
Medicinal Chemistry Research, 24(3), 1241–1264.
909.
6 QSAR and Machine Learning Predictors 159
84. Martin, T. M., Harten, P., Young, D. M., Muratov, E. N., Golbraikh, A., Zhu, H., & Tropsha,
A. (2012). Does rational selection of training and test sets improve the outcome of QSAR
modeling? Journal of Chemical Information and Modeling, 52(10), 2570–2578.
85. Puzyn, T., Mostrag-Szlichtyng, A., Gajewicz, A., Skrzyński, M., & Worth, A. P. (2011).
Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models.
Structural Chemistry, 22(4), 795–804.
86. Esbensen, K. H., & Geladi, P. (2010). Principles of proper validation: Use and abuse of
re-sampling for validation. Journal of Chemometrics, 24(3–4), 168–187.
87. Hawkins, D. M., Basak, S. C., & Mills, D. (2003). Assessing model fit by cross-validation.
Journal of Chemical Information and Computer Sciences, 43(2), 579–586.
88. Andrada, M. F., Vega-Hissi, E. G., Estrada, M. R., & Garro Martinez, J. C. (2017). Impact
assessment of the rational selection of training and test sets on the predictive ability of QSAR
models. SAR and QSAR in Environmental Research, 28(12), 1011–1023.
89. Wu, W., Walczak, B., Massart, D. L., Heuerding, S., Erni, F., Last, I. R., & Prebble, K. A.
(1996). Artificial neural networks in classification of NIR spectral data: Design of the training
set. Chemometrics and Intelligent Laboratory Systems, 33(1), 35–46.
90. Kronenberger, T., Windshügel, B., Wrenger, C., Honorio, K. M., & Maltarollo, V. G. (2018).
On the relationship of Anthranilic derivatives Structure and the FXR (Farnesoid X receptor)
agonist Activity. Journal of Biomolecular Structure and Dynamics, 36(16), 4378–4391.
91. Gomes, R. A., Genesi, G. L., Maltarollo, V. G., & Trossini, G. H. G. (2017). Quantitative
structure–activity relationships (HQSAR, CoMFA, and CoMSIA) studies for COX-2 selective
inhibitors. Journal of Biomolecular Structure and Dynamics, 35(7), 1436–1445.
92. Veríssimo, G. C., Pantaleão, S. Q., de Fernandes, P. O., Gertrudes, J. C., Kronenberger, T.,
Honorio, K. M., & Maltarollo, V. G. (2023). MASSA algorithm: An automated rational
sampling of training and test subsets for QSAR modeling. Journal of Computer-Aided
Molecular Design, 37(12), 735–754.
93. RDKit. https://www.rdkit.org/. Accessed 2022-03-08.
94. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher,
M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of
Machine Learning Research, 12(85), 2825–2830.
95. Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D.,
Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson,
J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J.,
Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., &
van Mulbregt, P. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in
python. Nature Methods, 17(3), 261–272.
96. Sydow, D., Morger, A., Driller, M., & Volkamer, A. (2019). TeachOpenCADD: A teaching
platform for computer-aided drug design using open source packages and data. Journal of
Cheminformatics, 11(1), 29.
97. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., Ohl, P., Sieb, C.,
Thiel, K., & Wiswedel, B. (2008). KNIME: The Konstanz information miner. In C. Preisach,
H. Burkhardt, L. Schmidt-Thieme, & R. Decker (Eds.), Data analysis, machine learning and
applications (Studies in classification, data analysis, and knowledge organization)
(pp. 319–326). Springer Berlin Heidelberg.
98. Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M.,
Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik,
M., & Zupan, B. (2013). Orange: Data mining toolbox in Python. Journal of Machine
Learning Research, 14, 2349–2353.
99. Frank, E., Hall, M. A., & Witten, I. H. (2016). The WEKA workbench. Morgan Kaufmann.
160 P. O. Fernandes and V. G. Maltarollo
100. Joshi, R., Zheng, Z., Agarwal, P., Hatmal, M. M., Chang, X., Seidler, P., & Haworth, I. S.
(2024). KNIME workflows for applications in medicinal and computational chemistry. Arti-
ficial Intelligence Chemistry, 2(1), 100063.
101. Nantasenamat, C., Worachartcheewan, A., Jamsak, S., Preeyanon, L., Shoombuatong, W.,
Simeon, S., Mandi, P., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2015). AutoWeka:
Toward an automated data mining software for QSAR and QSPR studies. In H. Cartwright
(Ed.), Artificial neural networks (pp. 119–147). Springer.
102. Ragno, R. (2019). www.3d-Qsar.Com: A web portal that brings 3-D QSAR to all electronic
devices—The Py-CoMFA web application as tool to build models from pre-aligned datasets.
Journal of Computer-Aided Molecular Design, 33(9), 855–864.
103. de Silverio, P. S. S. N., de Viana, J. O., & Barbosa, E. G. (2023). 3D-QSARpy: Combining
variable selection strategies and machine learning techniques to build QSAR models. Brazil-
ian Journal of Pharmaceutical Sciences, 59, e22373.
104. Gramatica, P., Chirico, N., Papa, E., Cassani, S., & Kovarich, S. (2013). QSARINS: A new
software for the development, analysis, and validation of QSAR MLR models. Journal of
Computational Chemistry, 34(24), 2121–2132.
105. Martins, J. P. A., Barbosa, E. G., Pasqualoto, K. F. M., & Ferreira, M. M. C. (2009). LQTAQSAR: A new 4D-QSAR methodology. Journal of Chemical Information and Modeling,
49(6), 1428–1436.
106. Teófilo, R. F., Martins, J. P. A., & Ferreira, M. M. C. (2009). Sorting variables by using
informative vectors as a strategy for feature selection in multivariate regression. Journal of
Chemometrics, 23(1), 32–48.
107. Martins, J. P. A., & Ferreira, M. M. C. (2013). QSAR modeling: um novo pacote
computacional open source para gerar e validar modelos QSAR. Química Nova, 36, 554–560.
108. Toropova, A. P., & Toropov, A. A. (2014). CORAL software: Prediction of carcinogenicity of
drugs by means of the Monte Carlo method. European Journal of Pharmaceutical Sciences,
52,21–25.
109. de Oliveira, D. B., & Gaudio, A. C. (2000). BuildQSAR: A new computer program for QSAR
analysis. Quantitative Structure-Activity Relationships, 19(6), 599–601.
110. Veerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C. P., & Agrawal, R. K. (2011).
Validation of QSAR models-strategies and importance. International Journal of Drug Design
& Discovery, 3, 511–519.
111. Pratim Roy, P., Paul, S., Mitra, I., & Roy, K. (2009). On two novel parameters for validation of
predictive QSAR models. Molecules, 14(5), 1660–1701.
112. Consonni, V., Ballabio, D., & Todeschini, R. (2009). Comments on the definition of the Q2
parameter for QSAR Validation. Journal of Chemical Information and Modeling, 49(7),
1669–1678.
113. Gramatica, P., & Sangion, A. (2016). A historical excursus on the statistical validation
parameters for QSAR Models: A clarification concerning metrics and terminology. Journal
of Chemical Information and Modeling, 56(6), 1127
114. Venkatraman, V., Chakravarthy, P. R., & Kihara, D. (2009). Application of 3D Zernike
descriptors to shape-based ligand similarity searching. Journal of Cheminformatics, 1(1), 19.
115. Avram, S. I., Crisan, L., Bora, A., Pacureanu, L. M., Avram, S., & Kurunczi, L. (2013).
Retrospective group fusion similarity search based on eROCE evaluation metric. Bioorganic
& Medicinal Chemistry, 21(5), 1268–1278.
116. Castillo-González, D., Mergny, J.-L., De Rache, A., Pérez-Machado, G., Cabrera-Pérez,
M. A., Nicolotti, O., Introcaso, A., Mangiatordi, G. F., Guédin, A., Bourdoncle, A., Garrigues,
T., Pallardó, F., Cordeiro, M. N. D. S., Paz-y-Miño, C., Tejera, E., Borges, F., & CruzMonteagudo, M. (2015). Harmonization of QSAR best practices and molecular docking
provides an efficient virtual screening tool for discovering new G-Quadruplex ligands. Journal
of Chemical Information and Modeling, 55(10), 2094–2110.
–1131.
6 QSAR and Machine Learning Predictors 161
117. Hanser, T., Barber, C., Marchaland, J. F., & Werner, S. (2016). Applicability domain:
Towards a more formal de finition$. SAR and QSAR in Environmental Research, 27(11),
865–881.
118. Serafim, M. S. M., Pantaleão, S. Q., da Silva, E. B., McKerrow, J. H., O’Donoghue, A. J.,
Mota, B. E. F., Honorio, K. M., & Maltarollo, V. G. (2023). The importance of good practices
and false hits for QSAR-driven virtual screening real application: A SARS-CoV-2 Main
protease (Mpro) case study. Frontiers in Drug Discovery, 3.
119. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes
decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.
120. Breiman, L. (2001). Random forests. Machine Learning, 45,5–32.
121. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the
predictions of any classi fier. In Proceedings of the 22nd ACM SIGKDD International Con-
ference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM.
122. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In
Advances in neural information processing systems (Vol. 30). Curran Associates, Inc.
123. Moreira-Filho, J. T., Neves, B. J., Cajas, R. A., de Moraes, J., & Andrade, C. H. (2023).
Artificial intelligence-guided approach for efficient virtual screening of hits against
Schistosoma Mansoni. Future Medicinal Chemistry, 15(22), 2033–2050.
124. Mushtaq, M., Usmani, S., Jabeen, A., Nur-e-Alam, M., Ahmed, S., Ahmad, A., & Ul-Haq,
Z. (2023). Identification of potent anti-immunogenic agents through virtual screening,
3D-QSAR studies, and in vitro experiments. Molecular Diversity.
125. Barbosa, H., Espinoza, G. Z., Amaral, M., de Castro Levatti, E. V., Abiuzi, M. B., Veríssimo,
G. C., de Fernandes, P. O., Maltarollo, V. G., Tempone, A. G., Honorio, K. M., & Lago,
J. H. G. (2024). Andrographolide: A diterpenoid from Cymbopogon schoenanthus identified
as a new hit compound against Trypanosoma cruzi using machine learning and experimental
approaches. Journal of Chemical Information and Modeling, 64(7), 2565–2576.
126. Fernandes, P. O., Dias, A. L. T., dos Santos Júnior, V. S., Sá Magalhães Serafim, M., Sousa,
Y. V., Monteiro, G. C., Coutinho, I. D., Valli, M., Verzola, M. M. S. A., Ottoni, F. M., de
Pádua, R. M., Oda, F. B., dos Santos, A. G., Andricopulo, A. D., da Silva Bolzani, V., Mota,
B. E. F., Alves, R. J., de Oliveira, R. B., Kronenberger, T., & Maltarollo, V. G. (2024).
Machine learning-based virtual screening of antibacterial agents against methicillinsusceptible and resistant staphylococcus aureus. Journal of Chemical Information and Model-
ing, 64(6), 1932–1944.
127. Wong, F., Zheng, E. J., Valeri, J. A., Donghia, N. M., Anahtar, M. N., Omori, S., Li, A.,
Cubillos-Ruiz, A., Krishnan, A., Jin, W., Manson, A. L., Friedrichs, J., Helbig, R., Hajian, B.,
Fiejtek, D. K., Wagner, F. F., Soutter, H. H., Earl, A. M., Stokes, J. M., Renner, L. D., &
Collins, J. J. (2023). Discovery of a structural class of antibiotics with explainable deep
learning. Nature, 626(7997), 177–185.
128. Rath, M., Wellnitz, J., Martin, H.-J., Melo-Filho, C., Hochuli, J. E., Silva, G. M., Beasley,
J.-M., Travis, M., Sessions, Z. L., Popov, K. I., Zakharov, A. V., Cherkasov, A., Alves, V.,
Muratov, E. N., & Tropsha, A. (2024). Pharmacokinetics Profi
opment, Validation, and implementation as a web tool for triaging compounds with undesired
pharmacokinetics profiles. Journal of Medicinal Chemistry, 67(8), 6508–6518.
129. White, A. D. (2023). The future of chemistry is language. Nature Reviews Chemistry, 7(7),
457–458.
130. Flam-Shepherd, D., Zhu, K., & Aspuru-Guzik, A. (2022). Language models can learn
complex molecular distributions. Nature Communications, 13(1), 3293.
ler (PhaKinPro): Model devel-