A Bright Future for Evolutionary Methods in Drug Design

Le, Tu C.; Winkler, David A.

doi:10.1002/cmdc.201500161

Cited by 32 publications

(32 citation statements)

References 27 publications

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“…QSAR analysis of the cytotoxicity data generated a computational prediction model that facilitated nanotube design (Fourches et al, 2015). A nano-combinatorial gold nanoparticle library aids discovery of cell-specific and high affinity binding nanoparticles that can distinguish between related cell types, so have potential applications in diagnostics and personalized medicine (Zhou et al, 2010; Le et al, 2015). …”

Section: Systematic Chemical and Physicochemical Modificationsmentioning

confidence: 99%

Toward a systematic exploration of nano-bio interactions

Bai

Liu

et al. 2017

Toxicology and Applied Pharmacology

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Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

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Section: Systematic Chemical and Physicochemical Modificationsmentioning

confidence: 99%

Toward a systematic exploration of nano-bio interactions

Bai

Liu

et al. 2017

Toxicology and Applied Pharmacology

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“…A synergistic combination of these accelerated experimental technologies with evolutionary algorithms provides a potentially disruptive change in the way molecules and materials are designed. Recent reviews describe the application of evolutionary approaches to drug and materials discovery [5–6]. …”

Section: Reviewmentioning

confidence: 99%

“…Two recent reviews have summarised how evolutionary methods have been used to discover and optimize drug leads [5], and materials [6]. …”

Section: Reviewmentioning

confidence: 99%

“…Many of these new and very useful metaheuristic methods, such as ant colony optimization, agent-based, evolutionary [5–6], and particle swarm algorithms, are indeed inspired by solutions that Nature has evolved to solve difficult problems [7]. We are also beginning to understand how to create artificial self-organized systems (reliant on the continuous input of matter and energy) that are ubiquitous in the natural world rather than the self-assembled systems that have been a major feature of contemporary nanotechnology [8–10].…”

Section: Introductionmentioning

confidence: 99%

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Biomimetic molecular design tools that learn, evolve, and adapt

Winkler

2017

Beilstein J. Org. Chem.

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A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

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“…Such models can be used to understand the relationships between the chemical structure of inhibitors and their efficacy and to allow the inhibition of compounds not yet synthesized or tested to be predicted. They can also be used as surrogate fitness functions for the evolutionary design of new corrosion inhibitors with multiple desirable properties [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Performance of Organic Corrosion Inhibitors

Winkler

2017

Metals

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Abstract:The withdrawal of effective but toxic corrosion inhibitors has provided an impetus for the discovery of new, benign organic compounds to fill that role. Concurrently, developments in the high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, and the increased capability of machine learning methods have made discovery of new corrosion inhibitors much faster and cheaper than it used to be. We summarize these technical developments in the corrosion inhibition field and describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. We briefly summarize the literature on quantitative structure-property relationships models of small organic molecule corrosion inhibitors. The success of these models provides a paradigm for rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys in diverse environments.

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A Bright Future for Evolutionary Methods in Drug Design

Cited by 32 publications

References 27 publications

Toward a systematic exploration of nano-bio interactions

Toward a systematic exploration of nano-bio interactions

Biomimetic molecular design tools that learn, evolve, and adapt

Predicting the Performance of Organic Corrosion Inhibitors

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