{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:5HZVWS3X2W3JRNM55LKC7QMZBK","short_pith_number":"pith:5HZVWS3X","canonical_record":{"source":{"id":"1502.02072","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-06T23:04:01Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"150bda5b8d1e214be71ac25fffa8e22e424b9c06df336cd22926fd60c45179fd","abstract_canon_sha256":"329f6dc2888ebf67aaa2b804b374b37e71b7790334bfe193d471cb5e9ecf424a"},"schema_version":"1.0"},"canonical_sha256":"e9f35b4b77d5b698b59dead42fc1990a9d66a52e62a0ac680c653d75fad25ff2","source":{"kind":"arxiv","id":"1502.02072","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.02072","created_at":"2026-05-18T02:27:50Z"},{"alias_kind":"arxiv_version","alias_value":"1502.02072v1","created_at":"2026-05-18T02:27:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.02072","created_at":"2026-05-18T02:27:50Z"},{"alias_kind":"pith_short_12","alias_value":"5HZVWS3X2W3J","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5HZVWS3X2W3JRNM5","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5HZVWS3X","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:5HZVWS3X2W3JRNM55LKC7QMZBK","target":"record","payload":{"canonical_record":{"source":{"id":"1502.02072","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-06T23:04:01Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"150bda5b8d1e214be71ac25fffa8e22e424b9c06df336cd22926fd60c45179fd","abstract_canon_sha256":"329f6dc2888ebf67aaa2b804b374b37e71b7790334bfe193d471cb5e9ecf424a"},"schema_version":"1.0"},"canonical_sha256":"e9f35b4b77d5b698b59dead42fc1990a9d66a52e62a0ac680c653d75fad25ff2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:27:50.027833Z","signature_b64":"BD6lCHlUGEhbufezpifGDaQf9EwOcMSFWU/JzeAYd19kQaTpoShQkBmR/ATIoGy5lEfpQsTXXYMUC8Gn97SlCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9f35b4b77d5b698b59dead42fc1990a9d66a52e62a0ac680c653d75fad25ff2","last_reissued_at":"2026-05-18T02:27:50.027464Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:27:50.027464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.02072","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:27:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I1qwQh9FzrpTVR2Q99U6PLi99ZpiBOlUhw10janZ3UohWjE5JsyeaKJNGbEE5clRds7N8L4RcmS/xoyW93rvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:39:40.689338Z"},"content_sha256":"3e93243c68f4965f65530b60081e1a652b8898dc7d84d06e95950027069d53cb","schema_version":"1.0","event_id":"sha256:3e93243c68f4965f65530b60081e1a652b8898dc7d84d06e95950027069d53cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:5HZVWS3X2W3JRNM55LKC7QMZBK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Massively Multitask Networks for Drug Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Bharath Ramsundar, Dale Webster, David Konerding, Patrick Riley, Steven Kearnes, Vijay Pande","submitted_at":"2015-02-06T23:04:01Z","abstract_excerpt":"Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.02072","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:27:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nLNp9DTu/Hp6emRYTE8FG+inF8UrJv9RLiYqH1yYuyK8nHLM7TYBTZin9AAxF7Fbhrm3cqCE5HHnfN9UHTvbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:39:40.689981Z"},"content_sha256":"acfd014f6a94153d6eec49ac4692369730a6290527e9d0d5ecae8e23a4cebada","schema_version":"1.0","event_id":"sha256:acfd014f6a94153d6eec49ac4692369730a6290527e9d0d5ecae8e23a4cebada"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5HZVWS3X2W3JRNM55LKC7QMZBK/bundle.json","state_url":"https://pith.science/pith/5HZVWS3X2W3JRNM55LKC7QMZBK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5HZVWS3X2W3JRNM55LKC7QMZBK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T00:39:40Z","links":{"resolver":"https://pith.science/pith/5HZVWS3X2W3JRNM55LKC7QMZBK","bundle":"https://pith.science/pith/5HZVWS3X2W3JRNM55LKC7QMZBK/bundle.json","state":"https://pith.science/pith/5HZVWS3X2W3JRNM55LKC7QMZBK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5HZVWS3X2W3JRNM55LKC7QMZBK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:5HZVWS3X2W3JRNM55LKC7QMZBK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"329f6dc2888ebf67aaa2b804b374b37e71b7790334bfe193d471cb5e9ecf424a","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-06T23:04:01Z","title_canon_sha256":"150bda5b8d1e214be71ac25fffa8e22e424b9c06df336cd22926fd60c45179fd"},"schema_version":"1.0","source":{"id":"1502.02072","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.02072","created_at":"2026-05-18T02:27:50Z"},{"alias_kind":"arxiv_version","alias_value":"1502.02072v1","created_at":"2026-05-18T02:27:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.02072","created_at":"2026-05-18T02:27:50Z"},{"alias_kind":"pith_short_12","alias_value":"5HZVWS3X2W3J","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5HZVWS3X2W3JRNM5","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5HZVWS3X","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:acfd014f6a94153d6eec49ac4692369730a6290527e9d0d5ecae8e23a4cebada","target":"graph","created_at":"2026-05-18T02:27:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the p","authors_text":"Bharath Ramsundar, Dale Webster, David Konerding, Patrick Riley, Steven Kearnes, Vijay Pande","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-06T23:04:01Z","title":"Massively Multitask Networks for Drug Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.02072","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3e93243c68f4965f65530b60081e1a652b8898dc7d84d06e95950027069d53cb","target":"record","created_at":"2026-05-18T02:27:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"329f6dc2888ebf67aaa2b804b374b37e71b7790334bfe193d471cb5e9ecf424a","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-06T23:04:01Z","title_canon_sha256":"150bda5b8d1e214be71ac25fffa8e22e424b9c06df336cd22926fd60c45179fd"},"schema_version":"1.0","source":{"id":"1502.02072","kind":"arxiv","version":1}},"canonical_sha256":"e9f35b4b77d5b698b59dead42fc1990a9d66a52e62a0ac680c653d75fad25ff2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9f35b4b77d5b698b59dead42fc1990a9d66a52e62a0ac680c653d75fad25ff2","first_computed_at":"2026-05-18T02:27:50.027464Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:27:50.027464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BD6lCHlUGEhbufezpifGDaQf9EwOcMSFWU/JzeAYd19kQaTpoShQkBmR/ATIoGy5lEfpQsTXXYMUC8Gn97SlCw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:27:50.027833Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.02072","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e93243c68f4965f65530b60081e1a652b8898dc7d84d06e95950027069d53cb","sha256:acfd014f6a94153d6eec49ac4692369730a6290527e9d0d5ecae8e23a4cebada"],"state_sha256":"6cfc1b2b146adf041d07320248134ebbf8125b322029b4407aa580c3e8981ca7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QV7IENU1pj41hn+4Eh1BFki2biJ0ikyit0mtNU/mK26cqq3lFkMG8OnPftPU+1Ja7fdxun/9CbyfDM2cihL3CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T00:39:40.693375Z","bundle_sha256":"fc01982fcbd4a19027ebf4c7fb1aee7099f6f489f46cf82e4b091e8ca719e0f7"}}