Contemporary Accounts in Drug Discovery and Development. Группа авторов
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A major step forward has been the development of advanced computational methods to explicitly enumerate many of the chemical modifications a project team might consider to improve the chemical matter and to further accurately score the properties of these ideated molecules. The enumeration of project relevant design ideas can be performed by way of a variety of methods including classical rules based approaches, as well as more modern machine‐learning and deep‐learning based techniques, and hybrid approaches [56–63]. Of equal importance to the ability to enumerate such synthesizable and project‐relevant design ideas is the ability to accurately evaluate their properties to facilitate the prioritization of this pool of molecules for synthesis. There are three major classes of scoring methods in common use:
1 Ligand‐based methods where a machine learning algorithm is provided the 2D structures of molecules with experimentally measured properties to train a model that can be used to predict the properties of other molecules based on this earlier provided information [64–67].
2 Approximate physics‐based methods, such as MM‐GB/SA interaction energy analysis and molecular docking, in which the likelihood of the molecule manifesting a particular property of interest will be estimated on the basis of the interactions it may form with its environment [39–41,68–75].
3 Rigorous physics‐based methods, such as free energy calculations, where all atomistic contributions to the property of interest will be explicitly simulated [76–82].
Each of these scoring methods have different strengths and weaknesses and attempt to balance prediction accuracy, computational efficiency, and breadth of applicability.
A key advantage to ligand‐based methods is the ease with which they can be employed to predict any property for which a representative training set can be assembled. This makes such methods especially attractive for ADMET property prediction, where many pharmaceutical companies have compiled extensive data sets for model parameterization [66, 83]. However, a key disadvantage of these methods is the extent to which model accuracy is expected to degrade as the evaluated molecules begin to differ substantially from the training set or manifest features not observed within the training set. Further, in practice it is quite difficult to determine whether or not a molecule under consideration for synthesis should be considered within or outside the domain of applicability of the parametrized ligand‐based model [84, 85]. Lastly, we note such methods are highly computationally efficient, and may require only milliseconds of CPU time per molecule.
In contrast to ligand‐based methods, approximate physics‐based methods, such as molecular docking and MM‐GB/SA scoring, do not require the construction of training sets and explicit parametrization, and instead proceed by way of an energetic analysis or three‐dimensional contact analysis to estimate whether the molecule under consideration for synthesis might achieve the discovery project design goals. This class of methods has been most extensively developed for protein‐ligand binding affinity analysis, and can be utilized to exclude molecules that are grossly sterically or electrostatically incompatible with the ligand binding site of the protein of interest, thereby enriching the quality of molecules under consideration by the discovery project team [68, 69]. This class of methods has also been adapted to address other ADMET properties such as membrane permeability, human serum albumin (HSA) binding, and hERG blockade, among others [86–90]. The computational efficiency of these methods is typically much lower and therefore more computationally expensive than ligand‐based approaches, and will often require seconds to minutes of CPU time. A key disadvantage of this approach is that quantitative prediction accuracy is typically not possible, and instead the methods will typically be used to triage large sets of molecules under consideration for further analysis, either by the project team members, or by way of more sophisticated computational techniques.
Rigorous physics‐based methods are distinguished from the earlier approximate methods by the explicit theoretically robust connection that can be made between the simulations performed and the experimentally measured quantities of interest. The benefit of this clear theoretical connection is that such methods can be quite accurate in practice, often with root‐mean‐square errors of ~1 kcal/mol, and do not require any system‐specific parametrization [76]. Such calculation accuracy enables these methods to more robustly support discovery project decision making than would be possible with less accurate or less reliable techniques. This improved accuracy and reliability though comes at the price of a greatly increased computational cost, typically ~1 GPU day of simulation time per scored molecule. As with the earlier mentioned approximate physics‐based methods, this class of methods has been most extensively developed for protein‐ligand binding affinity analysis [77–82]. Likewise, important work is ongoing to extend this class of methods to more comprehensively support ADMET property optimization, including solubility and binding selectivity [91, 92].
With a better understanding of these essential techniques in‐hand, we will now turn our attention to how these techniques have been used effectively by multiple parties to accelerate the discovery of novel and more efficacious drug therapies. We will illustrate these explanations through a variety of published case studies, which we hope will highlight the wide variety of ways such computational analyses can positively contribute to discovery project execution.
2.3 Illustrative Applications
2.3.1 Modeling Support of Target Validation, Feasibility Assessment, and Hit Discovery for Acetyl‐CoA Carboxylase
De novo fatty acid synthesis is rate limited by the enzyme ACC [93, 94]. A wealth of animal study data has suggested that inhibition of ACC might be palliative for NASH, which is believed to be an increasing cause of liver failure [95–102]. The development of a drug therapy to inhibit this target has been a highly nontrivial challenge, and conventional drug discovery efforts targeting the carboxyltransferase (CT) domain of ACC were found to yield drug candidates with poor physical chemical properties [93,103–110]. Nimbus therapeutics decided to take a different approach to drugging the target, and utilized a computational solvent analysis method, WaterMap, to investigate if it might be possible to develop improved drug‐like inhibitors that bind to the biotin carboxylase (BC) domain of the enzyme [111, 112]. These WaterMap calculations indicated the presence of multiple high energy hydration sites adjacent to an earlier known binding site of a natural product inhibitor of the BC domain, which might be exploited to design tight‐binding and ligand‐efficient chemical matter [112] (Figure 2.1).
This information was viewed as crucial by the project team to support initiation of the discovery project, since the presence of these high energy hydration sites was expected to greatly increase the likelihood the project team would be able to find satisfactory chemical matter [111]. The displacement of the water bound to these hydration sites was used as a constraint in virtual screening efforts, where only compounds expected to displace the water molecules were purchased and assayed. In agreement with the expectations of the computational modeling, the virtual screening campaign identified compound ND‐022 (Figure 2.2), which displaced the solvating water occupying these high‐energy hydration sites and was observed to bind to the target with 3.9 μM affinity [112]. Although in this case study, we have focused on the contributions that computational modeling made to the target validation, feasibility assessment, and hit discovery phases of this discovery project, ND‐022 was advanced into lead optimization, which resulted in the discovery of an exceptional development candidate molecule, ND‐630, which has been advanced into phase II clinical trials [4, 112, 113].