This two‐part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is ...increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to “discover” despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.
Exploring the possibilities: This two‐part Review evaluates the contribution of computer assistance and automation to various aspects of discovery in the chemical sciences. This second part reflects on exemplary case studies introduced in the first part. A set of open research directions in terms of both practical and methodological challenges are defined; addressing these would advance the extent to which these systems can make meaningful discoveries.
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•@TOME 3.0 interfaces protein structure modeling and ligand docking.•@TOME 3.0 allows virtual screening against multiple conformations of receptors.•@TOME 3.0 brings easy access to ...multiple scoring functions.•@TOME 3.0 brings a unique combination of modeling and docking.•@TOME 3.0 has been extensively validated.
Knowledge of protein–ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner.
@TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.
•Polypharmacological approaches provide a holistic overview of complex systems.•Structure–multiple activity relationships (SMARTs) are valuable in drug discovery.•Machine and deep learning benefit ...from SMARTs.•Multitarget algorithms allow the identification of side and off-target drug effects.•Polypharmacological approaches will guide the automation of drug design.
In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure–multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug–drug interactions.
This two‐part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical ...matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling. Part two reflects on these case studies and identifies a set of open challenges for the field.
The contribution of computer assistance and automation to various aspects of discovery in the chemical sciences is discussed in this two‐part Review. This first part describes how discoveries of physical matter, processes, and models are unified as search problems; what to consider when assessing the extent of autonomy in a discovery; and many examples of discoveries in synthetic chemistry, drug discovery, inorganic chemistry, and materials science.
•A data-driven medicinal chemistry model was explored in drug discovery.•Data science methods were used by medicinal chemists to varying extents.•Data analytics and visualization notably improved ...project time efficiency.•Predictive modeling was under-utilized but contributed to IP generation.•A concept was devised to educate next-generation medicinal chemists.
Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and efforts until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data science into practical medicinal chemistry and quantify the impact, as reported herein. Although characteristic features and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists
“Where do we go from here?” is the underlying question regarding the future (perhaps foreseeable) developments in computational chemistry. Although this young discipline has already permeated ...practically all of chemistry, it is likely to become even more powerful with the rapid development of computational hard‐ and software.
Fungal diseases have been underestimated worldwide but constitute a substantial threat to several plant and animal species as well as to public health. The increase in the global population has ...entailed an increase in the demand for agriculture in recent decades. Accordingly, there has been worldwide pressure to find means to improve the quality and productivity of agricultural crops. Antifungal agents have been widely used as an alternative for managing fungal diseases affecting several crops. However, the unregulated use of antifungals can jeopardize public health. Application of fungicides in agriculture should be under strict regulation to ensure the toxicological safety of commercialized foods. This review discusses the use of antifungals in agriculture worldwide, the need to develop new antifungals, and improvement of regulations regarding antifungal use.
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