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Introduction: The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field., Competing Interests: Authors SE, YI, IS, OM, AB, DS, VT, AM, AK, MM, DB, BZ, AA, and AZ are affiliated with Insilico Medicine. Insilico Medicine is a global clinical-stage commercial generative AI company with several hundred patents and patent applications and commercially available software. Insilico Medicine is a company developing an AI-based end-to-end integrated pipeline for drug discovery and development that is engaged in drug-discovery programs for aging, fibrosis and oncology., (Copyright © 2024 Evteev, Ivanenkov, Semenov, Malkov, Mazaleva, Bodunov, Bezrukov, Sidorenko, Terentiev, Malyshev, Zagribelnyy, Korzhenevskaya, Aliper and Zhavoronkov.)

This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD., Competing Interests: The authors declare the following competing financial interest(s): All authors work for Insilico Medicine, a commercial artificial intelligence company., (© 2023 The Authors. Published by American Chemical Society.)

Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20.

Baloxavir marboxil (BXM) is approved for treating uncomplicated influenza. The active metabolite baloxavir acid (BXA) inhibits cap-dependent endonuclease activity of the influenza virus polymerase acidic protein (PA), which is necessary for viral transcription. Treatment-emergent E23G or E23K (E23G/K) PA substitutions have been implicated in reduced BXA susceptibility, but their effect on virus fitness and transmissibility, their synergism with other BXA resistance markers, and the mechanisms of resistance have been insufficiently studied. Accordingly, we generated point mutants of circulating seasonal influenza A(H1N1)pdm09 and A(H3N2) viruses carrying E23G/K substitutions. Both substitutions caused 2- to 13-fold increases in the BXA EC50. EC50s were higher with E23K than with E23G and increased dramatically (138- to 446-fold) when these substitutions were combined with PA I38T, the dominant BXA resistance marker. E23G/K-substituted viruses exhibited slightly impaired replication in MDCK and Calu-3 cells, which was more pronounced with E23K. In ferret transmission experiments, all viruses transmitted to direct-contact and airborne-transmission animals, with only E23K+I38T viruses failing to infect 100% of animals by airborne transmission. E23G/K genotypes were predominantly stable during transmission events and through five passages in vitro. Thermostable PA-BXA interactions were weakened by E23G/K substitutions and further weakened when combined with I38T. In silico modeling indicated this was caused by E23G/K altering the placement of functionally important Tyr24 in the endonuclease domain, potentially decreasing BXA binding but at some cost to the virus. These data implicate E23G/K, alone or combined with I38T, as important markers of reduced BXM susceptibility, and such mutants could emerge and/or transmit among humans., Competing Interests: The authors have declared that no competing interests exist.