Less may be more: an informed reflection on molecular descriptors for drug design and discovery

Barnard, Trent; Hagan, Harry; Tseng, Steven; Sosso, Gabriele C.

doi:10.1039/c9me00109c

Cited by 14 publications

(14 citation statements)

References 42 publications

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“…Modeling systems are now available to predict synergism within a drug or other molecular combinations. − Such in silico tools are evolving fast and are recommended for screening purposes. Understanding the physicochemical attributes of encapsulable molecules based on molecular descriptors − can be a facile way to estimate the encapsulability, solubility, stability, and intermolecular interactions. Some online tools to calculate molecular descriptors, such as the Swiss ADME, are freely available.…”

Section: Perspectivesmentioning

confidence: 99%

Craft of Co-encapsulation in Nanomedicine: A Struggle To Achieve Synergy through Reciprocity

Bhattacharjee

2022

ACS Pharmacol. Transl. Sci.

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Achieving synergism, often by combination therapy via codelivery of chemotherapeutic agents, remains the mainstay of treating multidrug-resistance cases in cancer and microbial strains. With a typical core–shell architecture and surface functionalization to ensure facilitated targeting of tissues, nanocarriers are emerging as a promising platform toward gaining such synergism. Co-encapsulation of disparate theranostic agents in nanocarriersfrom chemotherapeutic molecules to imaging or photothermal modalitiescan not only address the issue of protecting the labile drug payload from a hostile biochemical environment but may also ensure optimized drug release as a mainstay of synergistic effect. However, the fate of co-encapsulated molecules, influenced by temporospatial proximity, remains unpredictable and marred with events with deleterious impact on therapeutic efficacy, including molecular rearrangement, aggregation, and denaturation. Thus, more than just an art of confining multiple therapeutics into a 3D nanoscale space, a co-encapsulated nanocarrier, while aiming for synergism, should strive toward achieving a harmonious cohabitation of the encapsulated molecules that, despite proximity and opportunities for interaction, remain innocuous toward each other and ensure molecular integrity. This account will inspect the current progress in co-encapsulation in nanocarriers and distill out the key points toward accomplishing such synergism through reciprocity.

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Section: Perspectivesmentioning

confidence: 99%

Craft of Co-encapsulation in Nanomedicine: A Struggle To Achieve Synergy through Reciprocity

Bhattacharjee

2022

ACS Pharmacol. Transl. Sci.

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“…A regularly used strategy is to utilise as many descriptors as possible to make predictions, however this is fallacious as it increases the likelihood of overfitting. 22 One descriptor that has been proven to be sufficient in offering an accurate representation of any given molecular structure is the smooth overlap of atomic positions (SOAP) descriptor. 26 Even though its most commonly used form only encodes up to three-body correlations, 27 the SOAP descriptor has been gaining popularity given its impressive performance across a plethora of widely different classes of materials and problems ranging from hydrogen absorption of nanoclusters 28 to the development of bespoke interatomic potentials.…”

Section: Introductionmentioning

confidence: 99%

Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor

Barnard

Tseng

Darby

et al. 2023

Mol. Syst. Des. Eng.

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“…Examples that focus on the solid form engineering applications relevant to the case study presented in this work include crystallisation propensity, CC propensity, solubility, and even amorphous properties such as glass forming ability [10][11][12][13][14][15][16]. Descriptors represent tangible molecular properties that a user can readily translate into practical terms which can be controlled experimentally to affect the application's target [17]. This is not a perfect scenario; however, as searching vast multidimensional inputs can make it difficult to gather data sufficiently large to cover the search space as well as making identification of specific important properties a challenge.…”

Section: Introductionmentioning

confidence: 99%

Images of chemical structures as molecular representations for deep learning

Wilkinson

Martinez-Hernandez

Wilson

et al. 2022

Journal of Materials Research

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Implementing Artificial Intelligence for chemical applications provides a wealth of opportunity for materials discovery, healthcare and smart manufacturing. For such applications to be successful, it is necessary to translate the properties of molecules into a digital format so they can be passed to the algorithms used for smart modelling. The literature has shown a wealth of different strategies for this task, yet there remains a host of limitations. To overcome these challenges, we present two-dimensional images of chemical structures as molecular representations. This methodology was evaluated against other techniques in both classification and regression tasks. Images unlocked (1) superior augmentation strategies, (2) application of specialist network architectures and (3) transfer learning, all contributing to superior performance and without prior specialised knowledge on cheminformatics required. This work takes advantage of image feature maps which do not rely on chemical properties and so can represent multi-component systems without further property calculations. Graphical abstract

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Less may be more: an informed reflection on molecular descriptors for drug design and discovery

Abstract: The phenomenal advances of machine learning in the context of drug design have led to the development of a plethora of molecular descriptors. And yet, there might be value in using just a handful of them – inspired by our physical intuition.

Cited by 14 publications

References 42 publications

Craft of Co-encapsulation in Nanomedicine: A Struggle To Achieve Synergy through Reciprocity

Craft of Co-encapsulation in Nanomedicine: A Struggle To Achieve Synergy through Reciprocity

Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor

Images of chemical structures as molecular representations for deep learning

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