Advancements in small molecule drug design: A structural perspective

Table Of Content

• Corwin Hansch dies at 92; scientist whose advances led to new drugs and chemicals
• Energy-based model
• Computer system predicts products of chemical reactions
• Workflow of the evolutionary design
• Separating science fact from fiction in Netflix’s ‘3 Body Problem’
• True shape of lithium revealed for the first time in UCLA research

Such generated 3D coordinates can be directly used for further simulation using quantum mechanics or by using docking methods. One of such first models is proposed by Niklas et al. [57], where they generate the 3D coordinates of small molecules with light atoms (H, C, N, O, F). They then use the 3D coordinates of the molecules to learn the representation to map it to a space, which is then used to generate 3D coordinates of the novel molecules.

Corwin Hansch dies at 92; scientist whose advances led to new drugs and chemicals

Reliable techniques for molecular property prediction and efficient search strategies are the building blocks for computer-aided molecular design8. Prediction models that can estimate the properties of given molecules can assist the virtual screening process in isolating candidate molecules with the desired properties9. Computational screening of molecules is dependent on the quality of virtual chemical libraries manually constructed from chemical databases10 or through combinatorial approaches11,12, and may induce uncertainty in the exploration of the appropriate chemical space.

Energy-based model

And as a matter of fact, these systematic metrics are a far cry from industry to discovery drugs, namely the generated molecules do not meet the requirement for the practical use. How to balance and unify two metrics systems for discovering drug in a faster and effective fashion runs tough at present. And designing the metrics for the practical use and combining with experiments will allow a major step towards molecular generation. Albeit wide application of GANs in some areas, the developments of GANs in generating molecular graphs are tender and delicate. Since averting likelihood-based loss functions, GAN sends molecular optimization hard stable.

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Computer system predicts products of chemical reactions

The use of deep neural network models to predict the properties of these molecules enabled more versatile and efficient molecular evaluations to be conducted by using the proposed method repeatedly. Four design tasks were performed to modify the light-absorbing wavelengths of organic molecules from the PubChem library. The first is the comprehensive databases, which usually contain diverse information such as biological activity, chemical structure and physical properties, including ZINC [30, 31], ChEMBL [32], PubChem [33] and DrugBank [34, 35] appeared in higher frequency. In particular, the data fields of drug in DrugBank can be linked to other databases like PubChem [36].

Workflow of the evolutionary design

In doing so, the model created new molecules, closely resembling the lead’s structure, averaging a more than 80 percent improvement in potency. For lead optimization, the model can then modify lead molecules based on a desired property. It does so with aid of a prediction algorithm that scores each molecule with a potency value of that property. In the paper, for instance, the researchers sought molecules with a combination of two properties — high solubility and synthetic accessibility.

These are particularly advantageous when exploring the chemical space for candidate molecules by not relying solely on the available molecular datasets. A thorough analysis of the proposed methods that includes benchmarking against the baseline models and investigating the efficacy of the molecular generation pipeline is also conducted. The proposed model learns the conditional distribution of molecular properties depending on their structural information allowing us to efficiently predict properties for a given molecule. Consequently, it learns the relationship between molecular composition and structure and its physiochemical properties, which is exploited by the proposed optimization technique to generate molecules exhibiting target properties. Previous deep learning methods for molecular generation do not always facilitate constrained sampling of molecular candidates6.

The molecules evolve through structural modifications, such as the addition, deletion, and substitution of atoms and substructures. As the number of generations increases, the structural changes accumulate, and a wider variety of moieties are introduced towards attaining the target property. These systems run on linear notations of molecules, called “simplified molecular-input line-entry systems,” or SMILES, where long strings of letters, numbers, and symbols represent individual atoms or bonds that can be interpreted by computer software. As the system modifies a lead molecule, it expands its string representation symbol by symbol — atom by atom, and bond by bond — until it generates a final SMILES string with higher potency of a desired property. In the end, the system may produce a final SMILES string that seems valid under SMILES grammar, but is actually invalid. Where Mt and St are the message and vertex update functions, whereas hvt and hvwt are the node and edge features.

Such a paradigm shift in the design of drugs is possible only because of recently developed deep generative model architectures. Here, we briefly discuss some of the breakthrough architectures along with the recent applications in drug discovery. Recently, molecular representations that can be iteratively learned directly from molecules have been increasingly adopted, mainly for predictive molecular modeling, achieving chemical accuracy for a range of properties [34,57,58]. Such representations as shown in Figure 3 are more robust and outperform expert-designed representations in drug design and discovery [59]. For representation learning, different variants of graph neural networks are a popular choice [37,60].

In this regards, several deep learning architectures have been used for efficient and accurate predictions of PLI parameters. These models vary among each other depending upon how protein or ligands are represented within the model [121,122,123,124]. For instance, Karimi et al. [125] proposed a semi-supervised deep learning model for predicting binding affinity by integrating RNN and CNN, wherein proteins are represented by an amino acid sequence and ligands in the form of SMILES strings. Other studies have used graph representations of ligand molecules with a string-based sequence representation of proteins [126,127].

All authors participated in drafting the manuscript and approved the final version. When loading a protein structure, MolView shows the asymmetric unit by default. When you are viewing large structures, like proteins, it can be useful to hide a certain part using fog or a clipping plane.

In this review, we have done our utmost to report different stages of molecular generation evolutionary path and highlight recent advances of research. Both of sequence-based and graph-based generative models have their own merits. The way in which molecular generative models are developed plays an important role for drug discovery and mirrors the evolution of deep neural networks in cross realm. Although substantial progress has been made, there is still large room for improving the performance of existing generative models and ameliorating the metrics of synthetic accessibility. These promotions of technologies and computing power promise to further advance the qualities of generating molecules with well-designed drug-like properties and make further efforts to accelerate the de novo drug design in a fully automated fashion.

In such scenarios, inverse design is of significant interest, where the focus is on quickly identifying novel molecules with desired properties in contrast to the conventional, so-called direct approach where known molecules are explored for different properties. In inverse design, we usually start with the initial dataset, for which we know the structure and properties, and map this to a probability distribution and then use it to generate new, previously unknown candidate molecules with desired properties very efficiently. Inverse design uses optimization and search algorithms [84,85] for the purpose and, by itself, can accelerate the lead molecule discovery process, which is the first step for any drug development. This paradigm holds even more promise when used in a closed loop with synthesis, characterization, and different test tools in such a way that each of these steps receives and transmits feedback concurrently, thus improving each other over time. This has shown some promise recently by substantially reducing the timeline for the commercialization of molecules from its discovery to days, which is otherwise known to span over a decade in most cases.