Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor.

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 µM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

Machine learning directed drug formulation development.

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

Anomalously Rapid Tunneling: Charge Transport across Self-Assembled Monolayers of Oligo(ethylene glycol).

This paper describes charge transport by tunneling across self-assembled monolayers (SAMs) of thiol-terminated derivatives of oligo(ethylene glycol) (HS(CH2CH2O)nCH3; HS(EG)nCH3); these SAMs are positioned between gold bottom electrodes and Ga2O3/EGaIn top electrodes. Comparison of the attenuation factor (ß of the simplified Simmons equation) across these SAMs with the corresponding value obtained with length-matched SAMs of oligophenyls (HS(Ph)nH) and n-alkanethiols (HS(CH2)nH) demonstrates that SAMs of oligo(ethylene glycol) have values of ß (ß(EG)n = 0.29 ± 0.02 natom-1 and ß = 0.24 ± 0.01 Å-1) indistinguishable from values for SAMs of oligophenyls (ß(Ph)n = 0.28 ± 0.03 Å-1), and significantly lower than those of SAMs of n-alkanethiolates (ß(CH2)n = 0.94 ± 0.02 natom-1 and 0.77 ± 0.03 Å-1). There are two possible origins for this low value of ß. The more probable involves hole tunneling by superexchange, which rationalizes the weak dependence of the rate of charge transport on the length of the molecules of HS(EG)nCH3 using interactions among the high-energy, occupied orbitals associated with the lone-pair electrons on oxygen. Based on this mechanism, SAMs of oligo(ethylene glycol)s are good conductors (by hole tunneling) but good insulators (by electron and/or hole drift conduction). This observation suggests SAMs derived from these or electronically similar molecules are a new class of electronic materials. A second but less probable mechanism for this unexpectedly low value of ß for SAMs of S(EG)nCH3 rests on the possibility of disorder in the SAM and a systematic discrepancy between different estimates of the thickness of these SAMs.

Charge Tunneling along Short Oligoglycine Chains.

This work examines charge transport (CT) through self-assembled monolayers (SAMs) of oligoglycines having an N-terminal cysteine group that anchors the molecule to a gold substrate, and demonstrate that CT is rapid (relative to SAMs of n-alkanethiolates). Comparisons of rates of charge transport-using junctions with the structure Au(TS)/SAM//Ga2O3/EGaIn (across these SAMs of oligoglycines, and across SAMs of a number of structurally and electronically related molecules) established that rates of charge tunneling along SAMs of oligoglycines are comparable to that along SAMs of oligophenyl groups (of comparable length). The mechanism of tunneling in oligoglycines is compatible with superexchange, and involves interactions among high-energy occupied orbitals in multiple, consecutive amide bonds, which may by separated by one to three methylene groups. This mechanistic conclusion is supported by density functional theory (DFT).