Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

Natural products contain scaffold structures that can be systematically exploited for the design of combinatorial compound libraries with druglike properties. We review approaches for scaffold identification, and compare properties and pharmacophoric features of drugs and natural products. In particular, an application of the self-organizing map technique is presented for natural product-derived compound and library design.

Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

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The potency of oxalyl amino acid derivatives as inhibitors of prolyl 4-hydroxylase was studied in vitro, in isolated microsomes and in chicken embryonic-tissue culture. These compounds represent structural analogues of 2-oxoglutarate in which the -CH2- moiety at C-3 is replaced by -NH-, with or without further structural modifications. The most efficient inhibitor of purified prolyl 4-hydroxylase was oxalylglycine. Its mode of inhibition was competitive with respect to 2-oxoglutarate. The Ki value varied between 1.9 and 7.8 microM, depending on the variable substrate used. Oxalylalanine inhibited purified enzyme with a Ki of 40 microM. Other oxalyl amino acid derivatives showed little inhibitory activity. In microsomes isolated from embryonic chicken bone, oxalylglycine and oxalylalanine inhibited prolyl hydroxylation with IC50 values of 23 and 120 microM respectively. Dimethyloxalylglycine was not an inhibitor of purified prolyl 4-hydroxylase and only weakly active in the microsomal system, but efficiently suppressed hydroxyproline synthesis in embryonic chicken calvaria and lung. The data suggest that dimethyloxalyl amino acids are converted into active inhibitors in intact cells, most likely in the cytoplasmic compartment.