Toward risk analysis of the impact of artificial intelligence on the deliberate biological threat landscape.

The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and AI. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how AI can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented.

Safeguarding Mail-Order DNA Synthesis in the Age of Artificial Intelligence.

Introduction: Artificial intelligence (AI) tools continue to be developed and used within the life sciences. The impact of these tools on the biosecurity landscape surrounding mail-order DNA synthesis and how to address the impacts have not been critically examined in the literature.

Methods: The impacts of AI-driven chatbots and biological design tools on the biosecurity landscape surrounding mail-order DNA synthesis were analyzed and described.

Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.

Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured.

Lessons Learned From Limited Overlap of 15 In Vitro COVID-19 Drug Repurposing Screens.

Drug repurposing can quickly and cost-effectively identify medical countermeasures against pathogens with pandemic potential and could be used as a down-selection method for selecting US Food and Drug Administration-approved drugs to test in clinical trials. We compared results from 15 high-throughput in vitro screening efforts that tested approved and clinically evaluated drugs for activity against SARS-CoV-2 replication. From the 15 studies, 304 drugs were identified as displaying the highest level of confidence from the individual screens.

A dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide.

The dataset presented here contains quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide collected via an AlphaSeq assay that can be used in the development and benchmarking of machine learning models. Starting from three seed sequences identified from a phage display campaign using a human naïve library, four sets of 29,900 antibodies were designed in silico by creating all k = 1 mutations and random k = 2 and k = 3 mutations throughout the complementary-determining regions (CDRs). Of the 119,600 designs, 104,972 were successfully built in to the AlphaSeq library and target binding was subsequently measured with 71,384 designs resulting in a predicted affinity value for at least one of the triplicate measurements.