{"doi":"10.1016/j.crmeth.2022.100254","title":"Computational counterselection identifies nonspecific therapeutic biologic candidates","abstract":"Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.","journal":"Cell Reports Methods","year":2022,"id":255504,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":20,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9489,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":783472,"name":"Ge Liu","orcid":"0000-0002-3864-9230","position":1,"is_corresponding":false},{"id":904371,"name":"Christine Banholzer","orcid":null,"position":2,"is_corresponding":false},{"id":904372,"name":"Geraldine Horny","orcid":null,"position":3,"is_corresponding":false},{"id":903704,"name":"Stefan Ewert","orcid":"0000-0003-3837-4713","position":4,"is_corresponding":false},{"id":300358,"name":"David K. Gifford","orcid":null,"position":5,"is_corresponding":false},{"id":563183,"name":"Sachit D. Saksena","orcid":"0000-0001-7009-7520","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T00:25:17.224217Z","pmid":"35880012","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}