Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

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Rethinking drug design in the artificial intelligence era

Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both.

Quantifying Drug Combination Synergy along Potency and Efficacy Axes.

Highly tumor-specific DNA nanostructures discovered by in vivo screening of a nucleic acid cage library and their applications in tumor-targeted drug delivery.

An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer - against all odds?

https://pubs.rsc.org/en/content/articlepdf/2019/cc/c9cc01429b

Second generation DNA-encoded dynamic combinatorial chemical libraries.

Background Hit identification is the starting point of small-molecule drug discovery and is therefore very important to the pharmaceutical industry. One of the most important approaches to identify a new hit is to screen a compound library using an in vitro assay. High-throughput screening has made great contributions to drug discovery since the 1990s but requires expensive equipment and facilities, and its success depends on the size of the compound library. Recent progress in the development of compound libraries has provided more efficient ways to identify new hits for novel drug targets, thereby helping to promote the development of the pharmaceutical industry, especially for firstin- class drugs. Methods A multistage and systematic research of articles published between 1986 and 2017 has been performed, which was organized into 5 sections and discussed in detail. Results In this review, the sources and classification of compound libraries are summarized. The progress made in combinatorial libraries and DNA-encoded libraries is reviewed. Library design methods, especially for focused libraries, are introduced in detail. In the final part, the status of the compound libraries at WuXi is reported. Conclusion The progress related to compound libraries, especially drug template libraries, DELs, and focused libraries, will help to identify better hits for novel drug targets and promote the development of the pharmaceutical industry. Moreover, these libraries can facilitate hit identification, which benefits most research organizations, including academics and small companies.

Compound Libraries: Recent Advances and Their Applications in Drug Discovery

The present invention relates to process of synthesizing alpha-aza spirocyclic medicine template. The improved technological process prepares alpha-aza spirocyclic medicine template with proline (m=1) or 2-nipecotic acid (m=2) derivative carrying protecting amido group and through four reaction steps, including amidation, oxidation, alkylation and hydrogenation. The improved technological process has less reaction steps, high yield, mild reaction condition and application value for large scale preparation of alpha-aza spirocyclic medicine template.

Preparation method of alpha-aza toroid drug template

The invention relates to a method for preparing 1,8-diazaspiro[4.5]decane with a protective group, and mainly solves the technical problems that the conventional synthetic route for the 1,8-diazaspiro[4.5]decane is long, the yield is low, an intermediate is difficult to purify, reaction conditions are rigorous, and mass production cannot be performed. The method comprises the following steps of: performing Michael addition reaction on N-PG-4-nitropyridine serving as a raw material and acrylic ester in alcoholic solution or acetonitrile to obtain N-PG-4-propanal-4-nitropyridine; and adding a catalyst into the product in an organic solvent, and performing hydrogenation reduction ring-closing reaction to obtain the 1,8-diazaspiro[4.5]decane with the protective group. The method has the advantages of a few reaction steps, high yield and mild conditions, and is a synthetic method with large-scale preparation value.

Method for synthesizing 1,8-diazaspiro[4.5]decane with protective group

Method for synthesizing 1,7-diazaspiro[4.5]decane with protective group

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Compound Libraries: Recent Advances and Their Applications in Drug Discovery

Preparation method of alpha-aza toroid drug template

Method for synthesizing 1,8-diazaspiro[4.5]decane with protective group

Method for synthesizing 1,7-diazaspiro[4.5]decane with protective group