Design of self-assembly dipeptide hydrogels andmachine learning via their chemical featuresFei Lia,1, Jinsong Hana,1, Tian Caob, William Lamc, Baoer Fand, Wen Tangd, Sijie Chena, Kin Lam Fokc,2, and Linxian Lia,2

aMing Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong; bDepartment of Computer Science, The University of North Carolina atChapel Hill, Chapel Hill, NC 27599; cSchool of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong; and dSouth ChinaAdvanced Institute for Soft Matter Science and Technology, South China University of Technology, Guangzhou 510640, China

Edited by Robert Langer, Massachusetts Institute of Technology, Cambridge, MA, and approved April 23, 2019 (received for review February 26, 2019)

Hydrogels that are self-assembled by peptides have attractedgreat interest for biomedical applications. However, the linkbetween chemical structures of peptides and their correspondinghydrogel properties is still unclear. Here, we showed a combina-tional approach to generate a structurally diverse hydrogel librarywith more than 2,000 peptides and evaluated their correspondingproperties. We used a quantitative structure–property relationshipto calculate their chemical features reflecting the topological andphysicochemical properties, and applied machine learning to pre-dict the self-assembly behavior. We observed that the stiffness ofhydrogels is correlated with the diameter and cross-linking degreeof the nanofiber. Importantly, we demonstrated that the hydro-gels support cell proliferation in culture, suggesting the biocom-patibility of the hydrogel. The combinatorial hydrogel library andthe machine learning approach we developed linked the chemicalstructures with their self-assembly behavior and can accelerate thedesign of novel peptide structures for biomedical use.

self-assembly | dipeptide hydrogels | machine learning

Hydrogels that are cross-linked by three-dimensional networksof modified molecules can maintain a large amount of water

without dissolving its own chemical structure, which is very similarto natural tissue. As a result of favorable biocompatibility,hydrogels have great potential in biomedical applications such asdrug delivery, tissue engineering, sensing, and cell encapsulation(1–7). In the past few years, considerable attention has been di-rected toward the design of peptide-based hydrogels in particular,not only because of their favorable features such as easy synthesis,decoration, biodegradability, and high compatibility, but also dueto their wide applications in the biological and medical fields (8–14). However, to the best of our knowledge, the prediction anddesign of peptide-based hydrogels is still challenging, which limitsour research choices on peptide-based hydrogels (15, 16). There-fore, the design strategy for hydrogels based on peptides is of greatsignificance. Our aim is to reveal the relationship between mo-lecular structure and hydrogel behavior, which can help us topredict and design peptide hydrogels with new chemical structures.There are approaches using molecular dynamics simulation

to model the self-assembly behavior of peptides into differenttypes of nanostructures, including nanofibers, which can sub-sequently form hydrogels (17–19). However, it is difficult toevaluate the actual prediction accuracy of the molecular dynamicssimulation methods because only a few positive peptides wereselected and synthesized to test whether they could form a hydro-gel. Additionally, the current reported synthetic method on 9-fluorenylmethyloxycarbonyl (Fmoc)-peptide is limited to thetraditional peptide synthesis method, involving step-by-stepprotection and deprotection. Since a high-throughput peptidegeneration method is not available, our first motive is to developa simple and fast method to generate a library with thousands ofpeptidelike molecules and then test their abilities to form ahydrogel. Using a rational complexation behavior from either acarboxylic acid or metal ions (20, 21), we plan to design chemicalstructures that can form a hydrogel at neutral pH without any

carboxylic acid groups and divalent or trivalent metal ions. Next,the structure–property relationship between the chemical struc-tures of peptides and their self-assembly properties can be exam-ined by introducing different chemical groups (other than carboxylicacids) into this peptide library.Deep learning or machine learning has been successfully ap-

plied to medical applications with accurate prediction; for ex-ample, in the diagnosis of pathology images (22–24). However,there are only a few reports on their application in the design oforganic materials, and typical prediction accuracy is lower than50% (25). Most of the work using machine learning for materialsdesign is reported in the field of energy, but reports on their usagefor biomaterials design are very limited. To our best knowledge,this is the first time that combinatorial chemistry and machinelearning have been used to predict the self-assembly behavior ofhydrogels. In this work, our second motive is to develop a machinelearning method to link the chemical features of peptides withtheir self-assembly properties and to predict the gel formation abilitybased on the two-dimensional chemical structure.In this work, we developed a peptidelike chemical library based

on a Ugi four-component reaction for screening the compoundsthat can form hydrogels. Selected hydrogels were characterizedwith a rheometer and transmission electron microscopy (TEM)and were further cultured with an adherent cell line. We gener-ated the chemical features of the whole chemical library and de-veloped the machine learning method to recognize these chemicalfeatures and predict whether a chemical structure can form ahydrogel at neutral pH without any divalent or trivalent metalions. We also summarize the relationship between the molecularstructure and gelation property.

Significance

Hydrogels maintain great potential for biomedical applications.However, predicting whether a chemical can form a hydrogelsimply based on its chemical structure remains challenging. Inthis study, we developed a combinational approach to obtain astructurally diverse hydrogel library with over 2,000 peptidesas a training dataset for machine learning. We calculated theirchemical features, including topological and physicochemicalproperties, and utilized machine learning methods to predictthe self-assembly behavior.

Author contributions: F.L., J.H., and L.L. designed research; F.L., J.H., W.L., B.F., W.T.,K.L.F., and L.L. performed research; F.L. and J.H. contributed new reagents/analytic tools;F.L., J.H., T.C., S.C., K.L.F., and L.L. analyzed data; and F.L., J.H., T.C., W.L., W.T., S.C., K.L.F.,and L.L. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Published under the PNAS license.1F.L. and J.H. contributed equally to this work.2To whom correspondence may be addressed. Email: ellisfok@cuhk.edu.hk or linxian.li@ki.se.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1903376116/-/DCSupplemental.

Published online May 20, 2019.

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Peptide-based hydrogels are usually formed based on the re-sponse of the carboxylic acid group toward the metal ions. In thispaper, we built a peptide-based library without a carboxylic acidgroup. For the construction of a comprehensive chemical libraryas a testing pool, we used 31 monomers, including 8 amines, 8aldehydes/ketones, 12 Fmoc-amino acids, and 3 isocyanides tosynthesize 2,304 compounds via the Ugi reaction as shown in Fig.1. The reaction was verified via mass spectrometry (MS) (SIAppendix, Figs. S30–S125) and 1H NMR (SI Appendix, Figs.S126–S135) of 96 selected compounds. After the completion ofthe reactions, organic solvents were removed and phosphatebuffered saline (PBS) solution was added to the reaction system.The solution was heated up to 80 °C and then cooled to roomtemperature to form hydrogels, as shown in Fig. 2E. Structure–property relationships of 81 hydrogels (Fig. 2 A–D and G)demonstrate that monomers A12, B7, C6, and D3 were the mostpossible gelling-like structures to form hydrogels (Fig. 2 A–D).We also studied the effect of the potential parameters onhydrogel properties, such as the numbers of hydrogen bond ac-ceptors (nHBAcc) and donors (nHBDon), the number of basicgroups (nBase), and the Ghose–Crippen LogKow (ALogP), asshown in Fig. 2 H–K. The results demonstrated that compounds

with lower nHBAcc, moderate nHBDon, no nBase, and higherALogP had stronger abilities to generate hydrogels. However,these features are not enough to predict whether a compoundcan form a hydrogel with a new chemical structure.Machine learning-based artificial intelligence (AI) has proved

to be useful for the prediction of human perception by employinga large number of psychophysical datasets (26, 27). However, theprediction for the formation of hydrogels is still challenging.Herein, machine learning was employed to explore the rela-tionship between the molecular skeleton and hydrogel propertiesfor the guidance of designing hydrogels.First, 2,304 separate chemical structures were produced based

on the monomers by the Ugi reaction for the construction ofa combinatorial library. Second, PaDEL-Descriptor (28) wasemployed for the calculation of molecular descriptors and finger-prints from the chemical library that contains all 2,304 structures;∼7,163,136 effective structural parameters (3,109 structural pa-rameters per molecule) were obtained according to the calculation.Next, data points were processed with machine learning al-

gorithms (Fig. 2F). We formulated our question as a binaryclassification problem (i.e., given the structural parameters foreach chemical structure): whether a hydrogel can be formed or

Fig. 1. Substrates for the construction of the hydrogel library.

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not. This problem is challenging because our data are highlyimbalanced. Only less than 4% (81/2,304) of the chemicalstructures can form hydrogels. To mitigate the class-imbalanceproblem, we introduced data resampling as a preprocessing step.Data resampling is a common approach to handle imbalanced

data. We used three common resampling approaches in ourmodels: random oversampling (RO), synthetic minority over-sampling technique (SMOTE), and adaptive synthetic sampling(ADASYN). RO is a naïve resampling approach which over-samples the minority class. The sampling strategy generates newsamples by randomly sampling with replacement from theavailable samples. After RO resampling, there are multiple du-plicated samples for certain data points. SMOTE is a syntheticsampling method which generates new samples from existingdata points. For a given data point in the minority class, SMOTEgenerates a new sample as a linear combination between the datapoint and one of its nearest neighbors from the same class (Fig.3B). ADASYN is an improved version of SMOTE. In ADASYN,the distribution of the minority class is considered in the sampling.

After data resampling, we applied multiple classificationmodels to our data. We applied an extensive list of classificationalgorithms, from the linear classifiers such as logistic regression,to the nonlinear classifiers such as a neural network. After tuningthe hyperparameters for each algorithm, we found three algo-rithms shown to possess the best prediction abilities (randomforest, gradient boosting, and logistic regression; Fig. 3 A and C–E), with gradient boosting being superior to the other two al-gorithms. We illustrate the precision–recall (PR) curves andreceiver operating characteristic (ROC) curves for differentmethods in Fig. 3. As our data are highly imbalanced (only 4% ofthe data can form hydrogels), we focused on precision and recallhere. Precision is the ratio of correct results to predicted results,while recall is the fraction of correct results in the predictedpositive samples. Our methods can achieve precisions of 54%,57%, and 62% for random forest, logistic regression, and gra-dient boosting, respectively, at the 50% recall. Moreover, featureimportance was calculated and the top 20 descriptors were obtainedfrom the best three machine learning algorithms (Fig. 4). The re-sults indicated that the descriptors monomer1 (Fmoc-amino acids),

Fig. 2. From screening to the rational design of hydrogels. Statistical data of monomers Fmoc-amino acids (A), amines (B), aldehydes or ketones (C ), andisocyanides (D) that formed gels. (E ) Method for the preparation of hydrogels. (F ) Design of machine learning methods. (G) Screening results of hydrogels(red, gel formed; gray, solution state). (H–K ) Correlation between hydrogel percentages with nHBAcc (H), nHBDon (I), nBase (J), and ALogP (K ).

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SpMax1_Bhi (largest absolute of Burden modified eigenvalue),and SpMin1_Bhi (smallest absolute of Burden modified eigen-value) contribute most to the formation of molecular hydrogels.The hydrogels with diversified functional groups can exhibit

different mechanical properties, which is important for con-trolled drug release and tissue engineering (29–32). We selectedtypical hydrogels from our algorithm with good temperature-responsive properties to study their rheological properties. Asshown in Fig. 5A (also see SI Appendix, Figs. S1–S11), thefrequency-dependent oscillatory rheology (γ0 = 0.5%, 0.1 to 100rad s−1) of selected hydrogels had certified hydrogel-like be-havior, where G′ was regnant in the whole process. Meanwhile,different chemical structures showed a variety of rheologicalproperties. Hydrogels 19/PBS, 10/PBS, 21/PBS, 20/PBS, and 64/PBS were selected based on the gradual increase of G′ and G″values. They displayed a different value of storage and loss ofoscillatory shear modulus (G′ and G″). These results reflected

their distinction on hardness and elasticity of hydrogels, dem-onstrating that the peptidelike molecules with multiple func-tional groups can lead to the difference in rheological behavior.Meanwhile, the mechanical properties (such as elasticity andviscosity) of substrates can influence the morphology, pro-liferation, and differentiation of stem cells. The increase of G′(elastic modulus) and G″ (viscous modulus) from compounds 19to 64 demonstrated that these hydrogels owned a large range ofmechanical properties that have potential application in stemcell research (33–35). These results also indicated that a series ofhydrogels with different rheological behaviors could be largelyobtained via a combinatorial approach.Since the microstructure of hydrogels can influence their rhe-

ological behavior, TEM experiments were performed to characterizetheir morphology. As shown in Fig. 5B, these compounds in PBSsolution exhibited an entangled fibrous network, which is ascribedto the supramolecular self-assembly of these compounds in PBS

Fig. 3. Machine learning algorithms for gel prediction. (A) Example of random forest and gradient boosting algorithms. (B) Illustration of SMOTE andADASYN oversampling algorithms. (C–E) PR curve and ROC curve calculated from the random forest model (C), the gradient boosting model (D), and thelogistic regression model (E).

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solution, leading to the formation of hydrogels. Interestingly,consistent with G′ and G″, it was easily observed that thedensity of nanofibers gradually increased from the 19/PBShydrogel to the 10/PBS and 21/PBS hydrogels. Meanwhile, incorroboration with their rheological behavior, an increase inthe density and the diameter of nanofiber was observed fromthe 21/PBS hydrogel to the 20/PBS and 64/PBS hydrogels.These results suggest that compounds with different functionalgroups exhibit differential self-assembly abilities and differen-tiated morphologies, which in turn leads to their distinct rheologicalproperties.

Finally, we tested the ability of hydrogels to support the cultureof TM4 cells, an adherent mouse Sertoli cell line with epithelialcell morphology. We labeled the cell body with CellTrackerGreen, and the cell nucleus with Hoechst 33342, to visualize thepotential changes in cell morphology. As shown in Fig. 6, at day 1after seeding, a subpopulation of cells in hydrogel 10- and 79-coated dishes demonstrated classical epithelial morphologies,whereas another subpopulation formed small cell clusters. Bothhydrogels 10 and 79 support the proliferation of cells as indicatedby the increase in cell number from day 1 to day 3 after seeding,suggesting the biocompatibility of these hydrogels.In conclusion, we have utilized a combinatorial approach to

establish a chemical library and a screen for hydrogel behavior.This approach is highly efficient, allowing high-throughputdesign and prediction to obtain hydrogels with novel chemicalstructures and controlled physical properties. We have de-veloped a machine learning approach to study the correlationbetween chemical features and the ability to form hydrogels ofthe peptidelike molecules. The machine learning revealed thatthe structure descriptors based on quantum chemistry exhibit a

Fig. 4. Feature importance (top-20 descriptors) from machine learning al-gorithms for gel prediction. (A–C) Top 20 parameters related to gel forma-tion calculated from random forest algorithm (A), gradient boostingalgorithm (B), and logistic regression (C).

Fig. 5. (A) Frequency-dependent (γ0 = 0.5%, 25 °C) oscillatory shear rhe-ology (Insets: photographs of hydrogels and chemical structures of com-pounds 19, 10, 21, 20, and 64, from left to right). (Magnification: 5×.) (B)TEM pictures of compound 19/PBS, 10/PBS, 21/PBS, 20/PBS, and 64/PBShydrogels (from left to right). (Scale bar: 1 μm.)

Fig. 6. Morphologies of cells cultured on AI-designed hydrogels. Repre-sentative images showing the morphologies of TM4 cells on indicatedhydrogels at day 1 (Left) and day 3 (Right) after seeding. Uncoated glass-bottom dish was used as a control. The cell body was stained with Cell-Tracker Green 5-chloromethylfluorescein diacetate (CMFDA, green) and thenucleus was stained with Hoechst 33342 (blue). (Magnification: 100×.)

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high correlation with gelling behavior. Importantly, we furthershowed that the hydrogels designed by this approach can beused in biomedical application such as cell culture. We envisionthat our combinatorial approach and machine learning methodcan be used as the design and prediction tools for peptidehydrogels with a controlled physical property for biomedicalapplications, such as drug delivery and tissue engineering.

Materials and MethodsMaterials and methods are detailed in SI Appendix.

ACKNOWLEDGMENTS. This project was supported by start-up funds fromMing Wai Lau Centre for Reparative Medicine, Karolinska Institutet; theNational Natural Science Foundation of China (Grant 81771639); the Re-search Grants Council of Hong Kong (Grants 14127316 and 14129316); andstart-up funds from the Lo Kwee Seong Foundation.

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