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In Silico Generation of Structural and Intermolecular Binding Affinity Data with Reasonable Accuracy: Expanding Horizons in Drug Discovery and Design

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Wei Li  *

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27 May 2024

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27 May 2024

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Abstract
In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.
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Subject: Biology and Life Sciences  -   Biophysics

1. Challenges and Limitations with Traditional Acquisition of Structural and Binding Affinity Data

Traditional experimental methods of acquiring structural and binding affinity (Kd) data for biological molecules in drug discovery pose significant challenges [1]. First, they entail substantial financial investments due to the need for specialized equipment, reagents, and skilled personnel. This financial burden often impedes smaller research laboratories and organizations with limited resources [2]. Moreover, traditional experimental techniques are time-consuming and labor-intensive. Techniques like X-ray crystallography and NMR spectroscopy require meticulous sample preparation, data collection, and analysis, spanning weeks or months. This prolonged timeline delays drug discovery efforts and hinders bringing new therapeutics to market [3,4,5]. Scalability and throughput are additional challenges. Methods such as X-ray crystallography and NMR spectroscopy are limited in scale, restricting simultaneous sample analysis. This limitation obstructs screening large compound libraries for potential drug candidates and exploring diverse molecular (e.g., chemical) space [6,7,8,9].
Furthermore, traditional techniques may struggle to accurately capture the dynamic nature of biological molecules [10]. Proteins and nucleic acids exhibit conformational flexibility and dynamic behavior, which traditional methods may not fully capture, providing only static snapshots of molecular structures [11]. Characterizing weak or transient interactions is another challenge. Kd, crucial in drug discovery, quantifies the strength of interaction between a ligand and its target. Techniques like SPR and ITC may struggle to measure binding affinities for weak or transient complexes, limiting the assessment of potential drug candidates’ efficacy and specificity [12,13]. In summary, traditional methods of acquiring structural and Kd data face significant challenges, including financial constraints, time intensiveness, scalability limitations, and difficulties in capturing dynamic interactions. These challenges necessitate alternative approaches to accelerate the drug discovery process [14,15].

2. Concept of In Silico Generation of Structural and Intermolecular Kd Data

On August 11, 2022, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time proposed in an MDPI preprint [8] and defined as below:
K d = f ( m o l e c u l e s , e n v P a r a )
where m o l e c u l e s represents the molecular system described either in strings (e.g., amino acid sequences, strings of SMILES to represent small molecules [16,17]), or in graphs to describe PTMs (e.g., glycosylated proteins) and PEMs (e.g., insulin icodec of Novo Nordisk [18,19,20]).
On October 19, 2023, the concept of GIBAC (Equation (1)) was for the first time updated, including its inception, definition (Equation (1)), construction, practical applications, technical challenges and limitations, and future directions [9,21]. As defined in [9], a real GIBAC (Equation (1)) is able to meet the criteria listed as below:
  • a real GIBAC needs to take genetic variations into account; and
  • a real GIBAC needs to work even without structural information; and
  • for a real GIBAC, a variety of factors need to be taken into account, such as temperature, pH [22,23], site-specific protonation states (e.g., side chain pKa of protein) [24,25], post-translational modifications (PTMs) [26,27,28], post-expression modifications (PEMs) [29,30], buffer conditions [31], et cetera; and
  • a real GIBAC requires a general forcefield for all types of molecules [3]; and
  • a real GIBAC requires a universal notation system for accurate and flexible description of all molecular types and drug modalities [10,32]; and
  • a real GIBAC is able to be used the other way around, i.e., to be used as a search engine for therapeutic candidate(s). With such a GIBAC-based search engine, a list of therapeutic candidates can be retrieved and ranked according to drug-target Kd value(s), with input parameters including drug target(s) and a desired drug-target Kd value or a range of it.
In [8] and [9], the concept of in silico generation of structural and intermolecular Kd data was for the first time proposed, where in silico data generation refers to the computational simulation and modeling of biological molecules and their interactions to generate synthetic structural and Kd data. Unlike traditional experimental methods, which rely on costly and time-consuming laboratory techniques, in silico approaches leverage computer algorithms and simulations to predict molecular structures, simulate molecular interactions, and estimate binding affinities (Kd) between molecules.

3. Rationale Behind In Silico Generation of Structural and Intermolecular Kd Data

The rationale behind using computational approaches for in silico data generation stems from several key advantages over traditional experimental methods. Firstly, computational methods offer a cost-effective and efficient alternative to laboratory experimentation. By harnessing the power of high-performance computing and sophisticated algorithms, researchers can perform complex simulations and modeling studies at a fraction of the cost and time required for experimental techniques [?]. Moreover, computational approaches provide unparalleled insights into the molecular mechanisms underlying biological processes and drug interactions as molecular structural modeling techniques allow researchers to explore the dynamic behavior of biomolecular systems with atomic-level precision, revealing crucial structural insights that may not be accessible through experimental methods alone [11,33].
Furthermore, computational approaches offer the flexibility to simulate and study complex biological systems in silico. Unlike experimental techniques, which may be limited by the availability of reagents or the feasibility of experimental setups, computational methods can simulate virtually any molecular system, from protein-ligand interactions to protein-protein complexes, in a highly controlled and customizable manner [34]. Overall, the use of computational approaches for in silico data generation represents a paradigm shift in drug discovery and design [?], offering unprecedented opportunities to accelerate the pace of research, optimize drug development pipelines, and advance our understanding of molecular biology and disease mechanisms [35,36].

4. Workflow Steps Involved in Generating Synthetic Structural Data and Intermolecular Kd Data

In short, the workflow for generating synthetic structural data and intermolecular Kd data through in silico methods typically involves two main steps: homology structural modeling (Modeller [37]) based on experimental structures deposited inside Protein Data Bank (PDB) [38,39,40,41] and structural biophysics-based calculations of intermolecular binding affinity (Kd) using Prodigy [34,42], as shown in Figure 1.
Specifically, the process begins with experimental structures deposited inside Protein Data Bank (PDB) [38], as shown in Figure 2:
  • the experimental complex structure of molecule A and molecule B (ABcomplex) is retrived from PDB.
  • amino acid sequences of molecules A and B are retrieved from the PDB file of ABcomplex.
  • amino acid sequences of molecules A and B are plugged into align.py of Modeller [37] to generate alignment files of ABcomplex (*.ali files) for each set of site-specific mutations for the amino acid sequences of molecules A and B. Here, for each set of site-specific mutations for the amino acid sequences of molecules A and B, the number of missense mutations are restricted to ensure that the overall accuracy of the subsequent structural modeling and Kd calculations.
  • alignment files of ABcomplex (*.ali files) are plugged into a set of in-house python scripts to produce a set of *ali files for variants of molecules A and B.
  • the structure of ABcomplex and the set of *ali files for variants of molecules A and B are plugged into build.py of Modeller [37] to generate a set of homology complex structural models for variants of molecules A and B for each set of site-specific mutations for the amino acid sequences of molecules A and B.
  • the PDB files of the set of homology complex structural models for variants of molecules A and B are plugged into Prodigy [34,42] to generate a set of Kd value for each set of site-specific mutations for the amino acid sequences of molecules A and B.

5. Synthetic Structural Data and Intermolecular Kd Data: Validation and Benchmarking

Validating the accuracy and reliability of in silico generated data is crucial to ensuring its usefulness and relevance in drug discovery and design [43,44]. Specifically, several strategies can be employed to validate the performance of the workflow and assess the quality of the generated data.
  • cross-validation is a widely used technique to assess the robustness of predictive models and evaluate their generalization performance. In the context of in silico data generation, cross-validation involves partitioning the dataset into training and validation sets and iteratively training the model on different subsets of the data. This process allows researchers to assess the model’s performance on unseen data and identify any potential biases or overfitting issues [45].
  • external validation involves testing the predictive model on an independent dataset that was not used during the model training phase. By evaluating the model’s performance on unseen data from different sources or experimental conditions, researchers can assess its ability to generalize and make accurate predictions in real-world scenarios. External validation provides a more rigorous assessment of the model’s performance and its applicability to diverse biological systems [46].
  • various performance metrics can be used to quantify the agreement between in silico predictions and experimental data. For structural data, metrics such as root-mean-square deviation (RMSD) or pairwise atomic distance distributions can assess the similarity between predicted and experimental structures. For Kd data, metrics such as Pearson correlation coefficient or mean absolute error can evaluate the accuracy of predicted binding affinities compared to experimental measurements [47,48].
  • benchmarking involves comparing the performance of the in silico workflow with other computational methods or experimental techniques. By benchmarking against established methods or gold standard datasets, researchers can assess the relative strengths and weaknesses of the workflow and identify areas for improvement. Benchmarking provides valuable insights into the performance of the workflow in comparison to existing approaches and helps establish its credibility and reliability in drug discovery applications [49].

6. Synthetic Structural Data and Intermolecular Kd Data: Applications and Implications

6.1. Data Augmentation and Training of Machine Learning Models

Synthetic data can also be used to augment real-world datasets, enhancing the diversity and representativeness of training data for machine learning models [50]. By generating additional samples with diverse structural conformations and binding affinities, synthetic data can help mitigate issues related to dataset imbalance and scarcity, thereby improving the generalization and performance of predictive models. Data augmentation techniques can enrich training datasets and enable more comprehensive exploration of chemical space, facilitating the discovery of novel drug candidates and optimizing lead compounds for specific therapeutic targets.
Thus, another of the key applications of synthetic data in drug discovery and design is training machine learning models for virtual screening and lead optimization. Synthetic structural data and Kd data can serve as valuable training examples for developing predictive models that identify potential drug candidates with high binding affinities and specificities for target proteins. By training machine learning models on synthetic data, researchers can build robust computational tools for accelerating the drug discovery process and prioritizing lead compounds for further experimental validation [51].

6.2. Broader Implications of Synthetic Structural Data and Intermolecular Kd Data

In recent years, the application of AI (including DL and ML) is becoming increasingly popular in drug discovery & design [51,52,53,54], particularly in lead optimization and ADMET studies, including carcinogenicity, hepatotoxicity, et cetera [52]. For instance, VenomPred is a promising solution for deriving structural toxicophores and assessing the safety profile of compounds.
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Figure 3. A variety of the practical application of GIBAC, including SME (small molecule inhibitor), Ab (antibody), Ag (antigen), XDC (antibody-drug conjugate (ADC), peptide-drug conjugate (PDC), aptamer-drug conjugate (ApDC) [55]), rPeptide (recombinant peptide drug), rProtein (recombinant protein drug), intrabodies [56], proteolysis-targeting chimeric molecules (PROTAC) [57,58], drug-drug interaction (DDI) [59], chimeric antigen receptor T (CAR-T) cell therapy [60].
Figure 3. A variety of the practical application of GIBAC, including SME (small molecule inhibitor), Ab (antibody), Ag (antigen), XDC (antibody-drug conjugate (ADC), peptide-drug conjugate (PDC), aptamer-drug conjugate (ApDC) [55]), rPeptide (recombinant peptide drug), rProtein (recombinant protein drug), intrabodies [56], proteolysis-targeting chimeric molecules (PROTAC) [57,58], drug-drug interaction (DDI) [59], chimeric antigen receptor T (CAR-T) cell therapy [60].
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Here, given the definition of GIBAC as in Equation (1), the discussion of the practical application of synthetic structural data and intermolecular Kd data in drug discovery & design focuses on intermolecular binding and interactions. In biological systems, there are a wide range of intermolecular binding pairs, including including enzyme-substrate [61,62], ligand-receptor [63,64], protein-protein [34,65], ion channel-drug [66,67], antibody-antigen [68,69,70], DNA-protein [71], RNA-protein [55,72], RNA-RNA [72], hormone-receptor [73], coenzyme-substrate [74], metal ion-protein [35,75], lipid-protein [76], et cetera. By definition, synthetic structural data and intermolecular Kd data can find its use for any binding pair involved in the molecular pathogenesis of human diseases, infectious or non-communicable, including:
  • ligand-receptor binding, e.g., insulin binding to its receptor [17];
  • protein-protein interaction, e.g., TNF-α binding to its receptor;
  • ion channel-drug interaction, e.g., verapamil binding to CaV1.2 [35];
  • antibody-antigen binding, e.g., Keytruda binding to PD-1 [77];
  • self-association and aggregation, e.g., formation of amyloid-β oligomer [78,79].

7. In Silico Generation of Structural and Intermolecular Kd Data: Future Directions

While the field of in silico generation of structural and intermolecular Kd data holds immense promise for advancing drug discovery and design efforts, researchers keep pushing the boundaries of computational biology and develop increasingly sophisticated methodologies, several exciting future directions emerge.

7.1. Integration of Multi-Scale Modeling

One promising direction for future research is the integration of multi-scale modeling approaches to capture the complex interplay between different levels of biological organization. By combining techniques such as quantum mechanics/molecular mechanics (QM/MM) simulations, coarse-grained modeling, and cellular simulations, researchers can simulate biological systems across multiple length and time scales, from individual atoms and molecules to entire cells or organisms. This multi-scale modeling framework enables a more comprehensive understanding of biological processes and interactions, facilitating the discovery of novel drug targets and optimization of therapeutic interventions [80,81].

7.2. Incorporation of Structural Dynamics

Incorporating structural dynamics into computational models is essential for capturing the inherent flexibility and conformational changes of biomolecular systems. While traditional static models provide valuable insights into molecular structure and interactions, they often overlook the dynamic nature of biological molecules. Future research directions may involve developing dynamic structural models that explicitly account for protein flexibility, ligand-induced conformational changes, and allosteric regulation. By integrating structural dynamics into computational models, researchers can better predict ligand binding modes, identify allosteric binding sites, and design more effective drugs targeting dynamic protein conformations [82,83,84].

7.3. Integration of Experimental and Computational Approaches

Finally, the integration of experimental and computational approaches represents a promising direction for advancing in silico data generation in drug discovery and design [85]. Combining experimental techniques such as cryo-electron microscopy, X-ray crystallography, and high-throughput screening with computational simulations and modeling enables researchers to leverage the strengths of both approaches and obtain comprehensive insights into biological systems [86,87]. Future research efforts may focus on developing hybrid experimental-computational workflows that seamlessly integrate experimental data with computational predictions, enabling synergistic exploration of drug-target interactions and accelerating the drug discovery process [85].

8. Conclusion

In conclusion, the in silico generation of structural and intermolecular Kd data offers a transformative approach to drug discovery and design. By integrating computational methodologies, this workflow (Figure 1, Figure 2 and Figure 4)enables the efficient generation of diverse datasets, predicting binding affinities accurately and expediting the identification of novel drug candidates beyond drug modality as a form factor. This approach fosters collaboration between computational and experimental researchers, accelerating research efforts and advancing pharmaceutical science. In summary, in silico generated data holds immense promise for revolutionizing drug discovery, enhancing efficiency, and driving innovation for the industry [8,9].

Author Contributions

Conceptualization, W.L.; methodology, W.L.; software, W.L.; validation, W.L.; formal analysis, W.L.; investigation, W.L.; resources, W.L.; data duration, W.L.; writing–original draft preparation, W.L.; writing–review and editing, W.L.; visualization, W.L.; supervision, W.L.; project administration, W.L.; funding acquisition, not applicable.

Funding

This research received no external funding.

Institutional Review Board Statement

No ethical approval is required.

Data Availability Statement

During the preparation of this work, the author used OpenAI’s ChatGPT in order to improve the readability of the manuscript, and to make it as concise and short as possible. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

Acknowledgments

The author is grateful to the communities of structural biology, biophysics, medicinal and computational chemistry and algorithm design, for the continued accumulation of knowledge and data for drug discovery & design, and for the continued development of tools (hardware, software and algorithm) for drug discovery & design.

Conflicts of Interest

The author declares no conflict of interest.

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Figure 1. Automated in silico generation of synthetic structural and Kd data.
Figure 1. Automated in silico generation of synthetic structural and Kd data.
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Figure 2. A detailed flowchart of automated in silico generation of synthetic structural and Kd data. This figure is a detailed version of Figure 1.
Figure 2. A detailed flowchart of automated in silico generation of synthetic structural and Kd data. This figure is a detailed version of Figure 1.
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Figure 4. A future direction of in silico generation of structural and intermolecular Kd data in drug discovery & design, and the pharmaceutical industry as a whole.
Figure 4. A future direction of in silico generation of structural and intermolecular Kd data in drug discovery & design, and the pharmaceutical industry as a whole.
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