Automated De Novo Drug Design: Are We Nearly There Yet?

Schneider, Gisbert; Clark, David E.

doi:10.1002/ange.201814681

Cited by 16 publications

(7 citation statements)

References 90 publications

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“…With greatly reduced amount of possible drug candidates, in-vitro cell experiments can further evaluate the efficacy of these molecules. In addition to virtual screening, de novo drug design methods [8], which generate synthesizable small molecules with high binding affinity, provide another type of computer-aided drug design direction. Artificial intelligence, e.g., machine learning and deep learning, is playing more and more important roles in the aforementioned computational methods and thus drug development [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Introductionmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…Likewise, de novo design ( Schneider and Schneider, 2016 ; Schneider and Clark, 2019 ) also serves to sample the vast chemical space for active molecules. As the identification of chemical motifs is often not restricted by availability of building blocks or pre-defined chemical reactions, designed molecules are often challenging to synthesize ( Hartenfeller et al, 2012 ; Gao and Coley, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods

et al. 2022

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The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into “drug-like” chemical space, such as target-activity machine learning models, respectively.

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“…As docking methods and computer power continue to improve, protein flexibility is not as obstructive as it once was, and new methods in the field all are able to address this. By providing novel scaffolds and constructive structural ideas, de novo design has aided medicinal chemists in developing patentable leads with desired properties . A great example of application is COVID-19, where de novo drug design methods made a rapid response to the newly discovered disease, yielding numerous novel drug molecules and reducing the time of development for treatment.…”

Section: Discussionmentioning

confidence: 99%

“…SA has been a consistent challenge for the field since its inception. Though a number of methods in the past decade have discovered chemical entities that eventually proceeded to experimental validation, manual alteration to proposed designs prior to actual synthesis is still a frequent occurrence . Many recent approaches have tried to resolve the SA issue, and their efforts will be discussed in this review.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Automated Structure-Based De Novo Drug Design

Tang,

Moretti,

Meiler

2024

J. Chem. Inf. Model.

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As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.

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Automated De Novo Drug Design: Are We Nearly There Yet?

Cited by 16 publications

References 90 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods

Recent Advances in Automated Structure-Based De Novo Drug Design

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